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The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China's largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people's daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase. [ABSTRACT FROM AUTHOR]

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO 2 emissions and non-CO 2 impacts, such as NO x emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NO x emitted, or per flown kilometre. In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NO x -induced ozone and H 2 O effects are well represented by the ACCF model output. The NO x -induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NO x -induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NO x -induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO 2 contribution observed in this study is low compared with the non-CO 2 effects, which requires further diagnosis. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study. [ABSTRACT FROM AUTHOR]

Among several devastating natural hazards, flooding is a common and serious threat to society causing huge loss of lives, properties, and infrastructure throughout the world. The intensity and frequency of this extreme weather event are expected to increase due to significant changes in the present‐day climate and land use and land cover (LULC) pattern. India has a very systematic and organized structural program and policies but lacks proper implementations, and adverse effect of climate change and the extreme event goes on in society. This paper is an analysis of floods in India and hazards due to climate change and LULC change patterns. Three models, namely "Eco‐biogeography‐based optimization (EBO), Random forest (RF), and Support vector machine (SVM)" were used to obtain the final output to prepare a "Flood susceptibility map". The result was validated through the "Receiver operating characteristics (ROC)" with "Area under curve (AUC)" values. The future rainfall scenario has been estimated by considering the "General circulation models" through different "shared socioeconomic pathways (SSPs)". The values of AUC are 0.915 (EBO), 0.887 (RF), and 0.869 (SVM), respectively. After consideration of different SSPs, the result shows that there is an increasing tendency of flood hazards in the projected period. Among all the employed modelling approaches, the EBO model has notable potential in delineating the possible flood‐prone regions for effective flood planning and management. Decision‐makers can benefit from country‐specific information and regional planner to implement sustainable and long‐term measures to overcome this type of hazardous situation. [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

This paper describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement: This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. [ABSTRACT FROM AUTHOR]

This paper describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement: This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. [ABSTRACT FROM AUTHOR]

Uncertainty sources in climate change impact studies can be addressed in two distinct phases, climate modelling and impact assessment. Generally, uncertainty from the climate modelling phase is the larger contributor and the impact phase uncertainty is sometimes marginalized. This paper aims to develop integrated uncertainty analysis approaches that deal with uncertainties at the impact level with all sources included. The existing uncertainty analysis method of Reliability Ensemble Averaging (REA) is modified for impact studies by bracketing the hydrological model parameter uncertainty. A novel probabilistic approach referred to as Bootstrapped Ensemble Uncertainty Modelling (BEUM) is also proposed for uncertainty analysis at the impact phase. Modified REA and BEUM are applied to streamflow projections of the Hulu Langat basin with uncertainties stemming from emission scenarios, general circulation models (GCM), downscaling method, and a hydrological model parametrization for a comprehensive integrated uncertainty assessment. The modified REA increased the reliability measure of each streamflow scenario and improved the uncertainty coverage percentage when compared with the original REA. The REA eliminated the projections of the less reliable GCMs, and therefore reduced uncertainty bands compared to the BEUM. The BEUM can create a large ensemble of streamflow projections through nonparametric bootstrapping of the limited impact scenarios and define the streamflow distribution for every quantile. Therefore, BEUM is able to encompass larger uncertainty intervals and explain the behavior of ensemble members through probabilistic distributions that can assist in decision making for climate change mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

This study was undertaken to evaluate how future uncertain climate-related hydrological responses and low and highflow frequencies affect local hydrology up to the 2100, depending on different Global Circulation Models (GCM) and different Concentration Scenarios (CS). To this end, daily total precipitation and daily mean temperature data were used for RCP4.5 and RCP8.5 CSs by employing GCMs (GFDL-ESM2M, HadGEM2-ES and MPI-ESM-MR). In the study, the years 1971–2000 were chosen as the baseline period. Future streamflow predictions were modeled by machine learning. The models were calibrated with baseline period data and future streamflow values were predicted by using the best Streamflow Prediction Model during the 2055s (2041–2070) and 2085s (2071–2100) periods. Flow Duration Curves from the streamflow predictions were obtained and by using them, discharges corresponding to 95% probability of exceedance for lowflow and 5% probability of exceedance for highflow were calculated. According to the findings, it is predicted that there will be decrease of approximately 21% in lowflow discharges and approximately 30% in highflow discharges. This prediction has suggested water resources managers to implement mitigation measures to climate change in Eastern Black Sea Basin and most probably in Türkiye, especially in the context of continuity of aquatic life and sustainable water supply. It is expected that the paper will take a very important place in estimating and evaluating the expected changes in basin hydrology in the coming years, especially in the highflows and lowflows, which are the key concepts in hydroelectric power generation and in flood management. • Development of streamflow prediction model for climatic projection data. • Streamflow estimation using limited input data in small catchment. • Obtaining far future streamflow for water resources pre-planning. • Discussion of the impact of climate change on daily streamflow extremes. [ABSTRACT FROM AUTHOR]

Global climate models, like any in silico numerical experiments, are affected by different types of bias. Uncertainty quantification remains a challenge in any climate detection and attribution analysis. A fundamental methodological question is to determine which statistical summaries, while bringing relevant signals, can be robust with respect to multimodel errors. In this paper, we propose a simple statistical framework that significantly improves signal detection in climate attribution studies. We show that the complex bias correction step can be entirely bypassed for models for which bias between the simulated and unobserved counterfactual worlds is the same as between the simulated and unobserved factual worlds. To illustrate our approach, we infer emergence times in precipitation from the CMIP5 and CMIP6 archives. The detected anthropogenic signal in yearly maxima of daily precipitation clearly emerges at the beginning of the twenty-first century. In addition, no CMIP model seems to outperform the others and a weighted linear combination of all improves the estimation of emergence times. Significance Statement: We show that the bias in multimodel global climate simulations can be efficiently handled when the appropriate metric is chosen. This metric leads to an easy-to-implement statistical procedure based on a checkable assumption. This allows us to demonstrate that optimal convex combinations of CMIP outputs can improve the signal strength in finding emergence times. Our data analysis procedure is applied to yearly maximum of precipitation from CMIP5 and CMIP6 databases. The attribution of the anthropogenic forcing clearly emerges in extreme precipitation at the beginning of the twenty-first century. [ABSTRACT FROM AUTHOR]

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO2 is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO2 radiative forcing and stronger positive feedbacks. Plain Language Summary: This paper describes the latest iteration of the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) climate model, which will be used for understanding historical climate change and to make projections for the future. We compare the model output to a wide range of observations over the recent era (1979–2014) and show that there has been a significant increase in how well the model performs compared to the previous version from 2014, particularly in the Southern Ocean, though some persistent biases remain. The model has a temperature response to the increase of carbon dioxide that is slightly higher than previous versions but is well within the range expected from observational and past climate constraints. Key Points: GISS‐E2.1 is an updated climate model version for use within the CMIP6 projectAtmospheric composition is calculated consistently in all model versionsResults demonstrate a significant improvement in skill in a climate model without changes to atmospheric resolution [ABSTRACT FROM AUTHOR]

Weather generators (WGs) are one of the major downscaling tools for assessing regional climate change impacts. However, some deficiencies in the performance of WGs have limited their usage. This paper presents a method for correcting the low-frequency variability (LFV) of precipitation in the improved weather generator (IWG) model. The method is based on bias correction in the monthly precipitation distribution of the generated daily series. The performance of the modified model was tested directly by comparing the statistics of generated and observed weather data for 14 stations, and also indirectly by comparing the characteristics of simulated stream-flows of a basin from the simulation run based on generated and observed weather data. The results showed that the method not only corrected the LFV of precipitation but also improved the reproduction of many other statistics. The provided IWG2 model can serve as a useful tool for the downscaling of general circulation model (GCM) scenarios to assess regional climate change impacts, especially hydrological effects. [ABSTRACT FROM AUTHOR]

Observations of warm past climates and projections of future climate change show that the Arctic warms more than the global mean, particularly during winter months. Previous work has attributed this reduced Arctic land seasonality to the effects of sea ice or clouds. In this paper, we show that the reduced Arctic land seasonality is a robust consequence of the relatively small surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission, without recourse to other processes or feedbacks. We use a general circulation model (GCM) with no clouds or sea ice and a simple representation of land. In the annual mean, the equator-to-pole surface temperature gradient falls with increasing CO2, but this is only a near-surface phenomenon and is not caused by the change in total meridional heat transport, which is virtually unaltered. The high-latitude land has about twice as much warming in winter than in summer, whereas high-latitude ocean has very little seasonality in warming. A surface energy balance model shows how the combination of the smaller surface heat capacity of land and the nonlinearity of the temperature dependence of surface longwave emission gives rise to the reduced seasonality of the land surface. The increase in evaporation over land also leads to winter amplification of warming over land, although amplification still occurs without it. While changes in clouds, sea ice, and ocean heat transport undoubtedly play a role in high-latitude warming, these results show that enhanced land surface temperature warming in winter can happen in their absence for robust reasons. [ABSTRACT FROM AUTHOR]

The reliability of general circulation models (GCMs) is commonly associated with their ability to reproduce relevant aspects of observed climate, and thus the evaluation of GCM performance has become a standard practice for climate change studies. As such, there is an ever-growing literature that focuses on developing and evaluating metrics to assess GCM performance. In this paper it is shown that some commonly applied metrics provide little information for discriminating GCMs based on their performance, once uncertainty is included. A new methodology is proposed that differs from common approaches in that it focuses on evaluating GCMs' abilities to reproduce the observed response of surface temperature to changes in external radiative forcing (RF), while controlling for observed and simulated variability. It uses formal statistical tests to evaluate two aspects of the warming trend that are central for climate change studies: 1) if the response to RF produced by a particular GCM is compatible with observations and 2) if the magnitudes of the observed and simulated rates of warming are statistically similar. We illustrate the proposed methodology by evaluating the ability of 21 GCMs to reproduce the observed warming trend at the global scale and for eight subcontinental land domains. Results show that most of the GCMs provide an adequate representation of the observed warming trend for the global scale and for domains located in the Southern Hemisphere. However, GCMs tend to overestimate the warming rate for domains in the Northern Hemisphere, particularly since the mid-1990s. [ABSTRACT FROM AUTHOR]

This paper investigates climate change impacts on the hydrology and water quality of Brazilian semi-arid reservoirs. An integrated approach coupling climate, hydrological and water quality models was proposed. Five general circulation models (GCMs) and two future scenarios (SSP2-4.5 and SSP5-8.5) were used for the projections. The simulations showed that uncertainty in climate forecast significantly impacted hydrological and water quality outputs. Some scenarios revealed a strong decrease in streamflow and storage volume. On the other hand, 40% of multi-GCM projections indicated reservoir eutrophication. The impacts on hydrology and water quality under SSP5-8.5 were more significant than those under SSP2-4.5. The results also indicated that drought events may be prolonged under climate change, principally at the end of the 21st century for SSP5-8.5, and may significantly increase the total phosphorus concentration. This suggests that tropical semi-arid reservoirs could be more vulnerable to eutrophication in scenarios of climate change than those in other regions. [ABSTRACT FROM AUTHOR]

Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. While the country-level scale of emissions changes can be estimated in near real time, the more detailed, gridded emissions estimates that are required to run general circulation models (GCMs) of the climate will take longer to collect. In this paper we use recorded and projected country-and-sector activity levels to modify gridded predictions from the MESSAGE-GLOBIOM SSP2-4.5 scenario. We provide updated projections for concentrations of greenhouse gases, emissions fields for aerosols, and precursors and the ozone and optical properties that result from this. The code base to perform similar modifications to other scenarios is also provided. We outline the means by which these results may be used in a model intercomparison project (CovidMIP) to investigate the impact of national lockdown measures on climate, including regional temperature, precipitation, and circulation changes. This includes three strands: an assessment of short-term effects (5-year period) and of longer-term effects (30 years) and an investigation into the separate effects of changes in emissions of greenhouse gases and aerosols. This last strand supports the possible attribution of observed changes in the climate system; hence these simulations will also form part of the Detection and Attribution Model Intercomparison Project (DAMIP). [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are routinely run under Atmospheric Modelling Intercomparison Project (AMIP) conditions with prescribed sea surface temperatures (SSTs) and sea ice concentrations (SICs) from observations. These AMIP simulations are often used to evaluate the role of the land and/or atmosphere in causing the development of systematic errors in such GCMs. Extensions to the original AMIP experiment have also been developed to evaluate the response of the global climate to increased SSTs (prescribed) and carbon dioxide (CO2) as part of the Cloud Feedback Model Intercomparison Project (CFMIP). None of these international modelling initiatives has undertaken a set of experiments where the land conditions are also prescribed, which is the focus of the work presented in this paper. Experiments are performed initially with freely varying land conditions (surface temperature, and soil temperature and moisture) under five different configurations (AMIP, AMIP with uniform 4K added to SSTs, AMIP SST with quadrupled CO2, AMIP SST and quadrupled CO2 without the plant stomata response, and increasing the solar constant by 3.3%). Then, the land surface temperatures from the free land experiments are used to perform a set of "AMIP prescribed land" (PL) simulations, which are evaluated against their free land counterparts. The PL simulations agree well with the free land experiments, which indicates that the land surface is prescribed in a way that is consistent with the original free land configuration. Further experiments are also performed with different combinations of SSTs, CO2 concentrations, solar constant and land conditions. For example, SST and land conditions are used from the AMIP simulation with quadrupled CO2 in order to simulate the atmospheric response to increased CO2 concentrations without the surface temperature changing. The results of all these experiments have been made publicly available for further analysis. The main aims of this paper are to provide a description of the method used and an initial validation of these AMIP prescribed land experiments. [ABSTRACT FROM AUTHOR]

The westerly jet (WJ) is an important component of atmospheric circulation, which is characterized by prominent seasonal variations in intensity and position. However, the response of the WJ over Asia during the Last Glacial Maximum (LGM) is still not clear. Using general circulation model experiments, the seasonal behaviors of the WJ over central Asia and Japan are analyzed in this paper. The results show that, compared to the present day (PD), the WJ presents a complicated response during the LGM, both in intensity and position. Over central Asia, it becomes weaker in both summer and winter. But over Japan, it is enhanced in summer but becomes diminished in winter. In terms of position, the WJ over central Asia shifts southward in both summer and winter, whereas the WJ over Japan moves southward in summer but does not change obviously relative to PD in winter. Such WJ changes are well explained by meridional temperature gradients in high troposphere, which is closely linked to seasonal thermal anomalies over the Tibetan Plateau (TP). Despite cooler LGM conditions, the anomalous warming center over the TP becomes stronger in summer. Derived from the heat budget equation, the stronger heating center is mainly caused by the weaker adiabatic cooling generated from ascending motion over the area south of the TP. In winter, the cooling over the TP is also strengthened, mostly owing to the subsidence-induced weaker adiabatic heating. Due to the importance of the WJ, the potential role of TP thermal effects should be a focus when explaining the East Asian climate change during the LGM. [ABSTRACT FROM AUTHOR]

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) observations in the North Atlantic since 1870 reveal a region of enhanced warming off the northeastern coast of North America, and a region of cooling to the south of Greenland. It has been hypothesized that these adjacent SST trends are a result of long‐term changes in the buoyancy‐driven ocean circulation—a slowdown of the Atlantic Meridional Overturning Circulation. The impacts of these historical SST trends on the atmosphere are estimated using idealized atmospheric general circulation model experiments in which the global atmosphere is exposed to modern climatological forcing minus the aforementioned regional SST trends. The local response includes a negative North Atlantic Oscillation tendency and southward shift of the wind forcing for the subtropical gyre. Due to planetary wave propagation, the regional SST trends also induce a northward shift of the intertropical convergence zone over the Indian Ocean. Implications for climate feedbacks and projections are discussed. Plain Language Summary: The ocean surface is warming over most of the planet, with a couple of notable exceptions. One is over the North Atlantic Ocean, just south of Greenland, where the temperature of the ocean surface has actually been cooling by about 1°C since 1870. Nearby, extending off the northeastern coastline of North America is a region where the ocean surface has been warming much faster than the global ocean on average—by about 1.5°C since 1870. This side‐by‐side pair of accelerated warming and cooling trends is likely due to a slowdown of the ocean's overturning circulation, which carries large amounts of heat energy from the tropics toward the poles. This paper reveals the effects of those unusual ocean temperature trends in the North Atlantic on the atmospheric circulation using a computer model of the global atmosphere. The model simulations indicate that these ocean temperature trends are causing shifts in the jet stream, changing the way the winds propel the upper ocean currents, and even have impacts quite far away by moving the tropical rain belt northward in the Indian Ocean. Key Points: The impact of historical sea surface temperature trends in the North Atlantic since 1870 is simulated with a global atmospheric modelAdjacent warming and cooling trends in the North Atlantic Ocean induce a negative NAO‐like response and southward‐shifted gyre forcingPropagation of planetary waves away from the North Atlantic induces a northward‐shifted intertropical convergence zone in the Indian Ocean [ABSTRACT FROM AUTHOR]

Air temperatures (Ta) are rising in a changing climate, increasing extreme temperature events. Examining how Ta increases are influencing extreme temperatures at the soil surface and belowground in the soil profile can refine our understanding of the ecological consequences of rising temperatures. In this paper, we validate surface and soil temperature (Ts: 0–100-cm depth) simulations in the SOILWAT2 model for 29 locations comprising 5 ecosystem types in the central and western USA. We determine the temperature characteristics of these locations from 1980 to 2015, and explore simulations of Ta and Ts change over 2030–2065 and 2065–2100 time periods using General Circulation Model (GCM) projections and the RCP 8.5 emissions scenario. We define temperature extremes using a nonstationary peak over threshold method, quantified from standard deviations above the mean (0-σ: an event > ∼ 51% of extreme events; 2- σ : > ∼ 98 % ). Our primary objective is to contrast the magnitude (∘C) and frequency of occurrence of extreme temperature events between the twentieth and twenty-first century. We project that temperatures will increase substantially in the twenty-first century. Extreme Ta events will experience the largest increases by magnitude, and extreme Ts events will experience the largest increases by proportion. On average, 2-σ extreme Ts events will increase by 3.4 ∘C in 2030–2065 and by 5.3 ∘C in 2065–2100. Increases in extreme Ts events will often exceed + 10 ∘C at 0–20 cm by 2065–2100, and at 0–100 cm will often exceed 5.0 standard deviations above 1980–2015 values. 2-σ extreme Ts events will increase from 0.9 events per decade in 1980–2015 to 23 events in 2030–2065 and 38 events in 2065–2100. By 2065–2100, the majority of months will experience extreme events that co-occur at 0–100 cm, which did not occur in 1980–2015. These projections illustrate the non-analog temperature increases that ecosystems will experience in the twenty-first century as a result of climate change. [ABSTRACT FROM AUTHOR]

Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. While the country-level scale of emissions changes can be estimated in near-real-time, the more detailed, gridded emissions estimates that are required to run General Circulation Models (GCM) of the climate will take longer to collect. In this paper we use recorded and projected country-and-sector activity levels to modify gridded predictions from the MESSAGE-GLOBIOM SSP2-4.5 scenario. We provide updated projections for concentrations of greenhouse gases, emissions fields for aerosols and precursors, and the ozone and optical properties that result from this. The codebase to perform similar modifications to other scenarios is also provided. We outline the means by which these results may be used in a model intercomparison project (CovidMIP) to investigate the impact of national lockdown measures on climate. This includes three strands: an assessment of short-term effects (5-year period), of longer-term effects (30 years) and an investigation into the separate effects of changes in emissions of greenhouse gases and aerosols. This last strand supports possible attribution of observed changes in the climate system, hence these simulations will also form part of the Detection and Attribution Model Intercomparison Project (DAMIP). [ABSTRACT FROM AUTHOR]

Abstract: The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Among other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific descriptions of these components are in companion papers) and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present‐day forcing. The suitability of the configuration for predictability on shorter time scales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2‐AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however, there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2‐AO, many aspects of the present‐day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extratropical variability and top‐of‐atmosphere and surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking. [ABSTRACT FROM AUTHOR]

Assessing the impacts of Land Use (LU) and climate change on future streamflow projections is necessary for efficient management of water resources. However, model projections are burdened with significant uncertainty arising from various sources. Most of the previous studies have considered climate models and scenarios as major sources of uncertainty, but uncertainties introduced by land use change and hydrologic model assumptions are rarely investigated. In this paper an attempt is made to segregate the contribution from (i) general circulation models (GCMs), (ii) emission scenarios, (iii) land use scenarios, (iv) stationarity assumption of the hydrologic model, and (v) internal variability of the processes, to overall uncertainty in streamflow projections using analysis of variance (ANOVA) approach. Generally, most of the impact assessment studies are carried out with unchanging hydrologic model parameters in future. It is, however, necessary to address the nonstationarity in model parameters with changing land use and climate. In this paper, a regression based methodology is presented to obtain the hydrologic model parameters with changing land use and climate scenarios in future. The Upper Ganga Basin (UGB) in India is used as a case study to demonstrate the methodology. The semi-distributed Variable Infiltration Capacity (VIC) model is set-up over the basin, under nonstationary conditions. Results indicate that model parameters vary with time, thereby invalidating the often-used assumption of model stationarity. The streamflow in UGB under the nonstationary model condition is found to reduce in future. The flows are also found to be sensitive to changes in land use. Segregation results suggest that model stationarity assumption and GCMs along with their interactions with emission scenarios, act as dominant sources of uncertainty. This paper provides a generalized framework for hydrologists to examine stationarity assumption of models before considering them for future streamflow projections and segregate the contribution of various sources to the uncertainty. [ABSTRACT FROM AUTHOR]

The spectral characteristics of paleoclimate observations spanning the last millennium suggest the presence of significant low-frequency (multi-decadal to centennial scale) variability in the climate system. Since this low-frequency climate variability is critical for climate predictions on societally-relevant scales, it is essential to establish whether General Circulation models (GCMs) are able to simulate it faithfully. Recent studies find large discrepancies between models and paleoclimate data at low frequencies, prompting concerns surrounding the ability of GCMs to predict long-term, high-magnitude variability under greenhouse forcing ( Laepple and Huybers, 2014a , 2014b ). However, efforts to ground climate model simulations directly in paleoclimate observations are impeded by fundamental differences between models and the proxy data: proxy systems often record a multivariate and/or nonlinear response to climate, precluding a direct comparison to GCM output. In this paper we bridge this gap via a forward proxy modeling approach, coupled to an isotope-enabled GCM. This allows us to disentangle the various contributions to signals embedded in ice cores, speleothem calcite, coral aragonite, tree-ring width, and tree-ring cellulose. The paper addresses the following questions: (1) do forward-modeled “pseudoproxies” exhibit variability comparable to proxy data? (2) if not, which processes alter the shape of the spectrum of simulated climate variability, and are these processes broadly distinguishable from climate? We apply our method to representative case studies, and broaden these insights with an analysis of the PAGES2k database ( PAGES2K Consortium, 2013 ). We find that current proxy system models (PSMs) can help resolve model-data discrepancies on interannual to decadal timescales, but cannot account for the mismatch in variance on multi-decadal to centennial timescales. We conclude that, specific to this set of PSMs and isotope-enabled model, the paleoclimate record may exhibit larger low-frequency variability than GCMs currently simulate, indicative of incomplete physics and/or forcings. [ABSTRACT FROM AUTHOR]

Water allocation institutions globally must operate within legal and political contexts established by precedent and codified in operating rules, even as they flex and adjust to climate change. California's Central Valley Water System (CVS) is a prime example. Recent global, national, regional, and local climate change assessments have highlighted climate-change-driven impacts on the CVS; however, these previous studies have not discussed the relative likelihood of performance decline, making it difficult to use the information for planning. In response, this paper presents a systematic climate change stress test that utilizes a physically based hydrologic model linked with a water resources system model representing the infrastructure, operations, and policy constraints of the interconnected system of natural river channels and man-made facilities that comprise the CVS. The results provide a summary of the sensitivity of the system to climate change, indicating the specific climate changes that cause performance of the system to decline below historical norms, and an estimation of the General Circulation Model (GCM) informed probability of those changes by 2050. Degraded performance is especially likely for State Water Project (SWP) deliveries (> 85%), and September carryover/drought storage in the Oroville Reservoir (the SWP's largest reservoir, ~ 95% likely to degrade). A decline in Net Delta Outflow is likely in all seasons except summer and early fall (when regulations require supplemental releases to combat salinity from sea level rise). For most of these metrics, the modeled performance drop is more severe in dry years than in wet years. [ABSTRACT FROM AUTHOR]

This paper demonstrates a climate change impact study on the hydrological process of a data-scarce Greek watershed. The Soil and Water Assessment Tool (SWAT) and, particularly, the ArcSWAT interface was used for the watershed simulation. The ERA-Interim reanalysis climate data regarding the period from 1981 to 2000 were used for the historical simulation of the watershed. The ArcSWAT simulated data were evaluated against the observed discharge data for the periods with the available data. The statistical evaluation confirmed the ArcSWAT model's capability in simulating the hydrological process of the research area. The climate change consequences on the hydrological components of the research area until the end of the twenty-first century were estimated by driving the ArcSWAT model with the Regional Climate Model Version 4 (RegCM4) forcing data under the extreme RCP 8.5 scenario, namely the simulations of the MPI and HadGEM2 general circulation models (GCMs), resulted from the spatio-temporal kriging approach. Based on the results, the increase in the minimum and the maximum temperature contributed to an increase in the actual evapotranspiration and the surface runoff. In contrast, the temperature increase caused a reduction in the infiltration. An increase (reduction) in the precipitation led to an increase (reduction) in the hydrological components. The climate change impact analysis of the Greek watershed showed that not only the precipitation changes but the temperature changes as well directly influence the water balance components of the research area and particularly the infiltration. [ABSTRACT FROM AUTHOR]

The aim of this paper is to estimate the effect that climate change will have on groundwater recharge at the Yucatan Peninsula, Mexico. The groundwater recharge is calculated from a monthly water balance model considering eight methods of potential and actual evapotranspiration. Historical data from 1961–2000 and climate model outputs from five downscaled general circulation models in the near horizon (2015–2039), with representative concentration pathway (RCP) 4.5 and 8.5 are used. The results estimate a recharge of 118 ± 33 mm·year–1 (around 10% of precipitation) in the historical period. Considering the uncertainty from GCMs under different RCP and evapotranspiration scenarios, our monthly water balance model estimates a groundwater recharge of 92 ± 40 mm·year–1 (RCP4.5) and 94 ± 38 mm·year–1 (RCP8.5) which represent a reduction of 23% and 20%, respectively, a result that threatens the socio-ecological balance of the region. [ABSTRACT FROM AUTHOR]

In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information. [ABSTRACT FROM AUTHOR]

Tropical cyclones generated in the North Atlantic and the Eastern Pacific are a constant hazard for Mexico. Along with a possible increased hazard of tropical cyclones due to global warming, there is an inescapable increase in vulnerability and disaster risk towards tropical cyclones due to population growth and coastal infrastructure developments. In Mexico, the Yucatan Peninsula has the highest landfall rates of major category hurricanes in addition to the highest rate of population growth in major tourist cities. Therefore, the assessment of landfalling tropical cyclones is of paramount importance for emergency management and planning. This paper provides an assessment of the future climate for landfalling tropical cyclones in the Yucatan Peninsula, based on synthetic tropical cyclones driven by atmospheric models (reanalysis and six different general circulation models (GCMs)) and under the Representative Concentration Pathway 8.5 climate change scenario. The results using the ensemble mean from the GCMs show that the Yucatan Peninsula will be more susceptible to more frequent intense hurricanes and more regular events undergoing rapid intensification. We conclude that even under the uncertainty imposed by the results, it is more likely than not that the future climate will bring more extreme events to this area. Therefore, it becomes imperative to implement strategic planning based on the characterization of tropical cyclone hazards framed within the assessment of global warming effects. [ABSTRACT FROM AUTHOR]

In this paper, trends of minimum and maximum temperatures in Iran were studied using time series of daily minimum and maximum temperatures of 45 meteorological stations from 1976 to 2005 (as the baseline period). Mann-Kendall test, for maximum and minimum temperature, was obtained 1.85 and 3.56, respectively, which was positive and significant. The slope of the trend line for maximum and minimum temperature was obtained 0.23 and 0.39 °C decade−1, respectively. In this study, the trend of extreme temperature indicators was also evaluated. According to the obtained results, in annual terms, TX10, FDO, TN10, and IDO indices have had a negative trend at most stations, but TX90, TR20, TNx, TNx, TXn, TN90, SDI, and SU25 indices showed a positive trend. In the seasonal scale, the indices of cold days (TX10) and cold nights (TN10) showed significant negative trends in most seasons. Warm days (TX90) and warm nights (TN90) showed significant positive trends at most stations. The results of future simulations using several general circulation models in different time periods showed that the highest increase in maximum and minimum temperature related to the RCP8.5 scenario in periods of 2071 to 2099. The results also showed that northwest of Iran would have the highest temperature rise. The results also showed that the probability density function of the minimum and maximum temperatures will shift to warmer temperatures. This could be an indication of climate change in the future decades in Iran. [ABSTRACT FROM AUTHOR]

The paper aims to achieve the modelling of climate change effects on heating and cooling in the building sector, through the use of the available Intergovernmental Panel on Climate Change forecasted data. Data from several different climate models will be fused with regards to mean air temperature, wind speed and horizontal solar radiation. Several climatic models data were analysed ranging from January 2006 to December 2100. Rather than considering each model in isolation, we propose a data fusion approach for providing a robust combined model for morphing an existing weather data file. The final aim is simulating future energy use for heating and cooling of a reference building as a consequence of the expected climate changes. We compare results, in terms of robustness to overfitting, for two different fusion methodologies, based on the comparison between errors on punctual historical data or prediction models that can be obtained by each climate simulator and by the actual ERA-INTERIM data set. Finally, we map the new aggregated data into a prediction trace of heating and cooling energy requirements. The expected energy demand is in the range of the one provided by single climate models, with a variability that reaches up to the 10% of the overall energy requirements. The approach proposed is an advancement as it allows to achieve better fits with existing re-analysis data if compared to specific global circulation models output data. Thus a more reliable estimation of energy use for heating and cooling can be achieved. [ABSTRACT FROM AUTHOR]

The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin. [ABSTRACT FROM AUTHOR]

• An accurate SWAT model is constructed by considering the influences of various human activities. • The influence of GCM uncertainty on climate change prediction is reduced through multi-model integration and bias correction. • Combined with SDSM model and SWAT model, it is concluded that the future temperature, precipitation and runoff of Yongding watershed will increase. Climate change has affected hydrological processes at global scale, and redistributed water resources in space and time. Anthropogenic activities even exacerbated the water resources problems. In this paper, Statistical DownScaling Model (SDSM) was used to downscale the predicted precipitation and temperature in the mountainous area of Yongding watershed under 4 SSP-RCP climate scenarios in 4 General Circulation Models (GCMs). Bayesian model averaging (BMA) method was employed for multi-model integration to reduce the impact of GCM uncertainty on climate change prediction. Then, the multi-model ensemble data were corrected by the Quantile mapping (QM) method. Soil and Water Assessment Tool (SWAT) was established in the study area considering the intensive human activities, including the water use in irrigation areas and regulation of the 8 large and medium-sized reservoirs. The SWAT model was applied to simulate the runoff response under the future climate change. The results showed that the maximum temperature (T max), minimum temperature (T min), precipitation (P) and runoff all show an upward trend under the four future climate scenarios. SSP585 scenario always showed the highest increase in temperature and precipitation. The simulated future annual runoff increases significantly compared with the reference period, indicating that the water shortage of the watershed may be alleviated in the future. The research results can provide a scientific basis for the decision-making of water resources management and protection in the watershed. [ABSTRACT FROM AUTHOR]

• The Med-ROMS model successfully simulated general circulation pattern in the Eastern Mediterranean Sea. • The changes associated with the 1997–1998 North Ionian reversal have been computed. • The RMSE difference between the Med-ROMS SSH and AVISO satellite SSH was about 0.0812 m. • We have observed high positive significant correlation between NAO indices 12 months moving average and Med-ROMS-SSHa -PC1. This paper deals with three hindcasting simulations of circulation and sea level elevation, using Med-ROMS (Mediterranean- Regional Ocean Model System, with high horizontal -resolution (8.6 km). Each simulation has started at the end of previous 100-years spin-up and control run. The model surface boundary conditions, from the corrected ERA-40 re-analyses (Pettenuzzo et al., 2010) and three momentum flux ensemble members are based on a surface wind Bayesian Hierarchical Models (BHM) - Mediterranean Forecasting System (MFS-Wind-BHM) described in Berliner and Kim (2007) and Milliff et al. (2011), cover the period January 1958 to December 2001. The results have been validated by an Empirical Orthogonal Function (EOF) analysis of surface sea height anomalies for satellite and Med-ROMS Hindcasts. The correlation coefficients between Med-ROMS (Principal Component) PC1 (Sea Surface Height anomaly) SSHa hindcasts 1, 2, 3 and AVISO satellite are 0.985, 0.97 and 0.98 respectively, all values are significant at 95% confidence limit. We have defined a Bimodal Oscillating System mechanism responsible for decadal reversals of the Ionian basin-wide circulation (BiOS) (Gacic et al., 2011, 2014) as a pattern of Eastern Mediterranean climate change. The ROMS model successfully simulated the observed general circulation pattern in the Eastern Mediterranean. Over the period from July 1997 till December 2001, the Med-ROMS has successfully simulated the north Ionian inversion; SSHa same as Satellite SSH with an averaged RMSE between them of 0.0792 m. The spatial correlation maps between wind stress curl and Med-ROMS-SSHa/EOF1-SSHa are made and found to be significantly correlated at a 95% confidence limit. The highest significant correlation coefficient for Med-ROMS-SSHa was approximately 0.8 and located off the western Turkish coast inside the Aegean Sea. This strong significant positive correlation reflects the cyclonic motion of Rhodes Gyre. A high significant correlation coefficient for Med-ROMS-EOF1-SSHa, of around 0.51, was found along the western coast of Greece in the northeast Ionian Sea. For the first time, we could correlate the North Ionian Sea inversion to wind stress curl, which was significantly correlated with the EOF1 Med-ROMS SSHa averaged hindcasts during the inversion period. In this transient period, we noticed a significant correlation coefficient, at a 95% confidence limit, between NAO indices moving on average over 12 months and Med-ROMS-SSHa -PC1 over the Eastern Mediterranean. [ABSTRACT FROM AUTHOR]

This paper studies the Upper Tigris River (UTR) drainage basin in Turkey for climate change impacted runoff estimations. Statistical downscaling method (SDM) is used by taking into consideration spatial dependence function (SDF) for the scenario precipitation projections at a set of available meteorology stations. Temporal adjustment between the climate scenarios and precipitation record time series is achieved by the white Markov (WM) stochastic process. Although various climate research center scenario data are considered, herein, only the general circulation model (GCM) A2 scenario data are adapted from the Hadley Center, England. The precipitation and runoff results are presented in decadal groups starting from 2001 to 2050 as cumulative monthly precipitation (CMP) and cumulative monthly runoff (CMR) graphs. It is observed that after 2021, precipitation decreases at about 12.5% and after 2030, it is 26%. Runoff projections indicate that they may decrease at about 30% especial after 2040. [ABSTRACT FROM AUTHOR]

Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. Published studies show that climate change is projected to have a substantial negative influence on the current suitable growing areas for indigenous Arabica in Ethiopia and South Sudan. Here we use all available future projections for the species based on multiple general circulation models (GCMs), emission scenarios, and migration scenarios, to predict changes in Extent of Occurrence (EOO), Area of Occupancy (AOO), and population numbers for wild Arabica coffee. Under climate change our results show that population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. EOO and AOO are projected to decline by around 30% in many cases. Furthermore, present‐day models compared to the near future (2038), show a reduction for EOO of over 40% (with a few cases over 50%), although EOO should be treated with caution due to its sensitivity to outlying occurrences. When applying these metrics to extinction risk, we show that the determination of generation length is critical. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria. Arabica coffee (Coffea arabica) is a key crop in many tropical countries and globally provides an export value of over US$13 billion per year. Wild Arabica coffee is of fundamental importance for the global coffee sector and of direct importance within Ethiopia, as a source of harvestable income and planting stock. In this paper we show that under climate change alone, population numbers could reduce by 50% or more (with a few models showing over 80%) by 2088. When applying the International Union for Conservation of Nature's Red list of Threatened Species (IUCN Red List) criteria, even with a very conservative generation length of 21 years, wild Arabica coffee is assessed as Threatened with extinction (placed in the Endangered category) under a broad range of climate change projections, if no interventions are made. Importantly, if we do not include climate change in our assessment, Arabica coffee is assessed as Least Concern (not threatened) when applying the IUCN Red List criteria. [ABSTRACT FROM AUTHOR]

Future sea surface temperature and sea-ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcings for the downscaling of future climate experiments. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly method and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new analogue method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiments and some real-case applications using observations. We find that with respect to other previously existing methods, the analogue method is a substantial improvement for the bias correction of future SIC. Consistency between the constructed SST and SIC fields is an important constraint to consider, as is consistency between the prescribed sea-ice concentration and thickness; we show that the latter can be ensured by using a simple parameterisation of sea-ice thickness as a function of instantaneous and annual minimum SIC. [ABSTRACT FROM AUTHOR]

Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [ABSTRACT FROM AUTHOR]

Highlights • High resolution catchment scale rainfall predictions were generated from 8 GCMs. • Multiple GCMs is preferable over a best model to account for the future variability. • Simulated climate change has limited impact on pollutant treatment performance. • Larger WSUD systems are recommended to account for the future variability. • Bigger rainwater tank is suggested for same harvesting reliability in the future. Abstract Water Sensitive Urban Design (WSUD) stormwater systems, also known as Low Impact Development (LID) systems or Nature Based Solutions (NBS), are currently implemented based on the underlying assumption of statistical stationarity of rainfall, which threatens to become outdated under climatic uncertainty. This paper applies a new downscaling method to examine the implications of climate change on future rainfall and evaluate the reliability of WSUD stormwater infrastructure in pollution reduction, flow frequency mitigation and reliability as an alternative water supply. A variety of future atmospheric scenarios are considered as part of this comprehensive assessment by analysing an ensemble of eight different downscaled General Circulation Models (GCMs). High resolution catchment-scale rainfall projections for Melbourne, Australia were generated using a scheme called High-resolution Downscaling of Rainfall Using STEPS (HiDRUS) at a fine 1 km and 6-min scale for more precise analysis with uncertainty estimates. Statistical analyses show that, in general, the climate models predict a drier future with fewer rainfall events and longer dry periods when comparing the simulated near future (2040–2049) periods against the base-line period (1995–2004). The difference simulated between historical and future rainfall projections show minimum difference of WSUD performance in pollution removal and flow frequency reduction, with slightly lower harvesting reliability (<3%) observed under future climate; high variabilities, however, were observed across GCM simulations, indicating big uncertainties of system reliability under various conditions, e.g. design wetland sizes may vary from 2.5% to 4.0% of the impervious catchment area according to different future projects across GCMs. Larger WSUD systems are recommended to ensure reliable performance of pollution removal, as well as harvesting reliability under simulated future conditions. The significance of considering an ensemble of different GCMs as opposed to many scenarios generated by a single 'best' climate model was also demonstrated for the robust estimation of uncertainty in future WSUD reliability. This work highlights important considerations for the future design, management and quantitative evaluation of WSUD reliability. [ABSTRACT FROM AUTHOR]

A comprehensive performance assessment of the empirical-statistical downscaling (ESD) technique named EPISODES is presented. Pertaining evaluation analyses consist of multifarious validation experiments as well as various comparisons of EPISODES' projections with those of three RCMs and two ESD methods based on the same GCM scenarios driven by two distinct representative concentration pathways (RCPs). EPISODES combines the downscaling of GCM simulations with a follow-up production of synthetic local time series. EPISODES is a comparably simple, computationally rather inexpensive technique, providing multi-variable and multi-site data that are suitable for being merged in an ensemble of RCM projections. This allows (e.g. for different RCPs) the compilation of large multi member ensembles derived from various GCM simulations via both main downscaling strategies (ESD and RCMs). Evaluation experiments reveal satisfying degrees of compliance between various results generated by EPISODES and observations. The grid cell bias for yearly values, for instance, is mostly less than 0.1∘C for temperature and 10% for precipitation totals. Recorded temperature values and precipitation totals corresponding to their 1st and the 99th percentiles are well represented by EPISODES too. Comparisons of various climate change signals derived by EPISODES and other downscaling approaches, present high levels of agreement as well. Many more findings referring to evaluation experiments and climate change projections are to be found in the paper as well as throughout the "Appendix". [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of projected future climate changes over China to the horizontal resolution of a regional climate model RegCM4.4 (RegCM), using RCP8.5 as an example. Model validation shows that RegCM performs better in reproducing the spatial distribution and magnitude of present-day temperature, precipitation and climate extremes than the driving global climate model HadGEM2-ES (HadGEM, at 1.875° × 1.25° degree resolution), but little difference is found between the simulations at 50 and 25 km resolutions. Comparison with observational data at different resolutions confirmed the added value of the RCM and finer model resolutions in better capturing the probability distribution of precipitation. However, HadGEM and RegCM at both resolutions project a similar pattern of significant future warming during both winter and summer, and a similar pattern of winter precipitation changes including dominant increase in most areas of northern China and little change or decrease in the southern part. Projected precipitation changes in summer diverge among the three models, especially over eastern China, with a general increase in HadGEM, little change in RegCM at 50 km, and a mix of increase and decrease in RegCM at 25 km resolution. Changes of temperature-related extremes (annual total number of daily maximum temperature > 25 °C, the maximum value of daily maximum temperature, the minimum value of daily minimum temperature in the three simulations especially in the two RegCM simulations are very similar to each other; so are the precipitation-related extremes (maximum consecutive dry days, maximum consecutive 5-day precipitation and extremely wet days’ total amount). Overall, results from this study indicate a very low sensitivity of projected changes in this region to model resolution. While fine resolution is critical for capturing the spatial variability of the control climate, it may not be as important for capturing the climate response to homogeneous forcing (in this case greenhouse gas concentration changes). [ABSTRACT FROM AUTHOR]

This paper compared future runoff projections using a Budyko type equation with respect to projections by a global hydrological model (GHM). The comparison was made for the annual mean runoff projections for a future period (2060-2100) after the Budyko parameter was set based on hydrologic model outputs at a present period (1960-2000). The objective of this study was to investigate the performance of the Budyko equation with respect to the hydrologic model at different climate regions. To address the question this study used the spatial average of runoff for the 35 largest basins in the world. According to the comparison, the projections by the two approaches agreed well (R2 = 0.983), in particular in humid tropic region (R2 = 0.986), but with consistent underestimation of future runoff (Median Error, ME = -0.042) by the Budyko equation. In subarctic region the performance of the Budyko equation was low (R2 = 0.599) due to the overestimation of future runoff (ME = 0.110). The results in the dry and temperate regions also showed some discrepancy (R2 = 0.931 and 0.724) without apparent patterns in the errors. The paper discussed possible reasons for the errors with respect to water and energy seasonality and changes in storage component contributions. [ABSTRACT FROM AUTHOR]

While it is generally recognized that the projected changes in the global climate will have serious negative consequences for the Caribbean as a whole, it is becoming more and more evident that the impacts of climate change will not be uniformly felt across the region. The primary aim of this paper is to provide a review of the Caribbean climate change literature. The paper begins with a brief discussion on the shifting nature of Caribbean vulnerability within the context of the region's longstanding and continued exposure to forces of global economic and environmental change. Particular attention is paid to both the practical and discursive dimensions of vulnerability, and the specific ways the term has been understood and framed in relation to other crosscutting themes such as adaptation and resilience in the broader academic literature. In addition to providing an overview of the regional climate change science literature, the paper offers a critical review of the existing climate change impacts literature for the Caribbean. By drawing on, and taking stock of this growing body of impact studies, the paper seeks to shed light on the differential and multi-scalar drivers of social and economic vulnerabilities to climate variability and change in the Caribbean as well as highlight some of the existing knowledge gaps that need to be addressed. The paper ends by providing some brief ref lections on the emerging themes arising from the discussion and highlights a few key areas for future academic research for the Caribbean in light of existing knowledge gaps observed in the regional climate change impact literature. [ABSTRACT FROM AUTHOR]

We explore the effects of seasonal variability for the climate of Earth-like planets as determined by the two parameters polar obliquity and orbital eccentricity using a general circulation model of intermediate complexity. In the first part of the paper we examine the consequences of different values of obliquity and eccentricity for the spatio-temporal patterns of radiation and surface temperatures as well as for the main characteristics of the atmospheric circulation. In the second part of the paper we analyse the associated implications for the habitability of planets close to the outer edge of the habitable zone (HZ). The second part focuses in particular on the multistability property of climate, i.e. the parallel existence of both an ice-free and an ice-covered climate state. Our results show that seasonal variability affects both the existence of and transitions between the two climate states. Moreover, our experiments reveal that planets with Earth-like atmospheres and high seasonal variability can have ice-free areas at much larger distance from the host star than planets without seasonal variability, which leads to a substantial expansion of the outer edge of the HZ. Sensitivity experiments exploring the role of azimuthal obliquity and surface heat capacity test the robustness of our results. On circular orbits, our findings obtained with a general circulation model agree well with previous studies based on one dimensional energy balance models, whereas significant differences are found on eccentric orbits. [ABSTRACT FROM AUTHOR]

Summary The effects of climate change on water resources have been studied extensively throughout the world through the use of hydrologic models coupled with General Circulation Model (GCM) output or climate sensitivity scenarios. This paper examines the effects of hydrologic model parameterization uncertainty or equifinality, where multiple unique hydrologic model parameter sets can result in adequate calibration metrics, on hydrologic projections from downscaled GCMs for three snowmelt-dependent watersheds (upper reaches of the Clearwater, Gunnison, and Sacramento River watersheds) in the western United States. The hydrologic model used in this study is the Soil and Water Assessment Tool (SWAT) and is calibrated for discharge at the watershed outlet in each watershed. Despite achieving similar calibration metrics, a majority of hydrologic projections of average annual streamflow during the 2080s were statistically different, with differences in magnitude and direction (increase or decrease) compared to historical annual streamflows. At the average monthly time-scale, a majority of the hydrologic projections varied in peak streamflow timing, peak streamflow magnitude, summer streamflows, as well as overall increases or decreases compared to the historical monthly streamflows. Snowmelt projections from the SWAT model also widely varied, both in depth and snowmelt peak timing, for all watersheds. Since a large portion of the runoff-producing regions in the western United States is snowmelt-dependent, this has large implications for the prediction of the amount and timing of streamflow in the coming century. This paper shows that hydrologic model parameterizations that give similar adequate calibration metrics can lead to statistically significant differences in hydrologic projections under climate change. Therefore, researchers and water resource managers should account for this uncertainty by assembling ensemble projections from both multiple parameter sets and GCMs. [ABSTRACT FROM AUTHOR]

The extent to which climate conditions influenced the spatial distribution of hominin populations in the past is highly debated. General circulation models (GCMs) and archaeological data have been used to address this issue. Most GCMs are not currently capable of simulating past surface climate conditions with sufficiently detailed spatial resolution to distinguish areas of potential hominin habitat, however. In this paper, we propose a statistical downscaling method (SDM) for increasing the resolution of climate model outputs in a computationally efficient way. Our method uses a generalised additive model (GAM), calibrated over present-day climatology data, to statistically downscale temperature and precipitation time series from the outputs of a GCM simulating the climate of the Last Glacial Maximum (19 000-23 000 BP) over western Europe. Once the SDM is calibrated, we first interpolate the coarse-scale GCM outputs to the final resolution and then use the GAM to compute surface air temperature and precipitation levels using these interpolated GCM outputs and fine-resolution geographical variables such as topography and distance from an ocean. The GAM acts as a transfer function, capturing non-linear relationships between variables at different spatial scales and correcting for the GCM biases. We tested three different techniques for the first interpolation of GCM output: bilinear, bicubic and kriging. The resulting SDMs were evaluated by comparing downscaled temperature and precipitation at local sites with paleoclimate reconstructions based on paleoclimate archives (archaeozoological and palynological data) and the impact of the interpolation technique on patterns of variability was explored. The SDM based on kriging interpolation, providing the best accuracy, was then validated on present-day data outside of the calibration period. Our results show that the downscaled temperature and precipitation values are in good agreement with paleoclimate reconstructions at local sites, and that our method for producing fine-grained paleoclimate simulations is therefore suitable for conducting paleo-anthropological research. It is nonetheless important to calibrate the GAM on a range of data encompassing the data to be downscaled. Otherwise, the SDM is likely to overcorrect the coarse-grain data. In addition, the bilinear and bicubic interpolation techniques were shown to distort either the temporal variability or the values of the response variables, while the kriging method offered the best compromise. Since climate variability is an aspect of the environment to which human populations may have responded in the past, the choice of interpolation technique is therefore an important consideration. [ABSTRACT FROM AUTHOR]