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Dynamical downscaling (DD) consists in using archives of Coupled Global Climate Models (CGCM) simulations as the atmospheric and sea-surface boundary conditions (BC) to drive nested, Regional Climate Model (RCM) simulations. Biases in the CGCM-generated driving BC, however, can have detrimental impacts on RCM performance. It is well documented for the historical period that CGCM-simulated sea-surface temperatures (SST) suffer substantial biases, especially important near coastal regions. Assuming that these SST biases are time-invariant, they could in principle be subtracted from century-long CGCM projections before being used to drive RCMs. This paper investigates the performance of a 3-step DD approach as follows. The CGCM-simulated sea-surface temperatures (SST) are first empirically corrected by subtracting their systematic biases; the corrected SST are then used as ocean surface BC for an atmosphere-only GCM (AGCM) simulation; finally this AGCM simulation provides the atmospheric lateral BC to drive an RCM simulation. This is what we refer to as the 3-step approach CGCM–AGCM–RCM of DD, which can be compared to the traditional 2-step approach CGCM–RCM consisting of driving an RCM simulation directly by CGCM-generated BC. In this paper we compare the results obtained with the two approaches, for present and future climates under RCP8.5, using the fifth-generation Canadian Regional Climate Model (CRCM5) with a grid mesh of 0.22° over the North American CORDEX domain, driven by two CMIP5 models: the Canadian Earth System Model of the Canadian Centre for Climate modelling and analysis (CanESM2) and the Earth System Model of the Max-Planck-Institut fur Meteorologie (MPI-ESM-MR). The results show that, in current climate, the seasonal-mean 2-m temperature fields simulated with the 3-step DD have generally smaller biases with respect to the observations than those simulated with the 2-step DD; in fact the performance of the 3-step DD simulations often approaches that of the reanalyses-driven simulation. For the seasonal-mean precipitation field, however, the differences between the two DD methods are not conclusive. Differences between the projected climate changes with the two DD methods vary substantially depending upon the variable being considered. Differences are particularly important for temperature: over the bulk of the North American continent, the 3-step DD projects more warming in winter and less in summer. This result highlights the nonlinearities of the climate system, and constitutes an additional measure of uncertainty with DD.

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.

Since 2006 the scientific community in California, in cooperation with resource managers, has been conducting periodic statewide studies about the potential impacts of climate change on natural and managed systems. This Special Issue is a compilation of revised papers that originate from the most recent assessment that concluded in 2009. As with the 2006 studies that influenced the passage of California’s landmark Global Warming Solutions Act (AB32), these papers have informed policy formulation at the state level, helping bring climate adaptation as a complementary measure to mitigation. We provide here a brief introduction to the papers included in this Special Issue focusing on how they are coordinated and support each other. We describe the common set of downscaled climate and sea-level rise scenarios used in this assessment that came from six different global climate models (GCMs) run under two greenhouse gas emissions scenarios: B1 (low emissions) and A2 (a medium-high emissions). Recommendations for future state assessments, some of which are being implemented in an on-going new assessment that will be completed in 2012, are offered.

The paper is based on recent climate simulations provided by the leading world climate centres and available through the Data Distribution Centre of the Intergovernmental Panel on Climate Change. Seven models have been considered. Three experiments: control, and two integrations for different greenhouse gases emission scenarios have been analysed. Assessments have been made for the European window defined as 5–40° E, 40–60° N. The paper presents some selected results for the air surface temperature and precipitation. This is not a study on evaluation of models, detailed intercomparisons between models can be found in other works (e.g., Barnett, 1999; Boer and Lambert, 2000; Lambert and Boer, 2001), as well as on Internet (e.g., a web-site of the Program for Climate Model Diagnosis and Intercomparison1). The intention of the authors was to show examples of analyses made specifically for the Central European region. For the reason of technical limitations of the paper only some results can be presented.

Climate change leads to a more frequent occurrence of hot days (HDs) and hot nights (HNs). The consecutive occurrence of HDs and HNs (COHs) is often used as a measure of the persistence of an extremely hot spell. Nonetheless, the combined effect of air temperature and relative humidity on the changing COHs has never been studied. In this paper, we use an ensemble of global climate models and multiple thermal indices to robustly examine the combined effect of air temperature and relative humidity on COHs globally on an hourly basis. Our findings reveal that COHs show an increasing trend in the future and a strong latitudinal gradient increasing from high latitudes to the equator. Compared to COHs based on air temperature, the frequency of COHs based on perceived temperature is amplified by the combined effects of high temperature and humidity for both boreal and austral summers. To investigate the underlying mechanisms, we examine two different diurnal temperature ranges (DTRs), derived from air temperature and perceived temperature, for their corresponding types of COHs. Both DTRs are projected to increase in the future relative to the historical period from 1980 to 2004, but the DTR changes derived from perceived temperature are consistently larger than those derived from air temperature. Due to the nonlinearity in thermal indices, the perceived temperature in HDs and HNs rising faster than air temperature leads to a larger increase in perceived COHs. The COHs are further amplified by the increasing number of HNs and HDs that occur consecutively under wet conditions.

This paper compiles indices for the El Nino—Southern Oscillation (ENSO) and seven unforced decadal-to-multidecadal climate modes (Interdecadal Pacific Oscillation (IPO), Tripole Pacific Index (TPI), Pacific Decadal Oscillation focused over North Pacific (PDO) and South Pacific (SPDO), North Atlantic Multidecadal Oscillation (AMO), South Atlantic Multidecadal Oscillation (SAMO), and Indian Ocean basin (IOB)) in a 100-member ensemble of the Max Planck Institute Earth System Model (MPI-ESM1.1) and a 35-member ensemble of Community Earth System Model (CESM1). Comparison among various detrending approaches indicates that the best approach to remove the unforced component is by subtracting the ensemble average temperature at each grid point from the original model output. Similar characteristics are investigated in the indices of both MPI-ESM1.1 and CESM1 ensemble models. The results further indicate no statistically significant lead-lag correlations between the unforced multidecadal climate modes originating from North Pacific (e.g., PDO) and North Atlantic (e.g., AMO), suggesting that a high correlation found in previous observational studies may be due to biased detrending approaches.

The accuracy of the climate impact models depends on the representative power of the climate model used in the analysis. Many problems in these impact models can be eliminated by the selection of the appropriate climate models as an input. The main purpose of this research is to find a methodology to select the more representative climate models. In recent years, the quantity and quality of climate models have significantly improved. In several projects, many different Global Climate Model (GCM)–Regional Climate Model (GCM) combinations have been carried out. One of these recent comprehensive projects is CORDEX. The monthly temperature outputs of various GCM-RCM combinations over Europe between the years 1976 and 2005 were used in this paper for the determination of the more representative combinations. E-OBS is used as the observation data set in this research. Using the proposed method, a clear difference between the more, moderately, and less representative model combinations could be obtained. To model the future temperature climatology of Europe, the best choice of the analysis method is using MPI as GCM and either REMO or CCLM5 as RCM over the whole domain. According to our analysis, the best representative climate model combination can be performed by running MPI-CCLM5 combination over the EURO-CORDEX domain.

The prediction of internal solitary waves (ISWs) propagation is a difficult problem in the field of oceanography due to the complexity of its generative mechanism and the lack of in-situ data. In this paper, the simulation data is used to establish a prediction model of ISWs propagation in the southern Andaman Sea by deep learning. The global tidal model TPXO7.2 is used to obtain the semi-diurnal tidal velocity data at the generation source of ISWs. The generation and propagation characteristics of ISWs are obtained by the Massachusetts Institute of Technology General Circulation Model (MITgcm). The generative location, amplitude, and propagation characteristics of the ISWs are calculated based on MITgcm. The database is set up by the extracted data from MITgcm. Then, the prediction model of ISWs propagation is established using long and short-term memory (LSTM) networks. Finally, the accuracy of the model is verified by the ISWs Moderate Resolution Imaging Spectroradiometer (MODIS) images of the southern Andaman Sea and in-situ data. The results demonstrate that, compared with the MODIS images, the average relative error of time predicted by the LSTM model is 17.81%, and the average absolute error is 0.95 h. The average relative error of longitude predicted by the LSTM model is 0.10%, and the average absolute error is 0.10°. The average of the amplitudes predicted by the LSTM model is 56.99 m. The in-situ data of the amplitude of ISWs is 60 m in the previous research. The two amplitudes are relatively close.

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 $>\sim $ 51% of extreme events; 2- $\sigma :>\sim 98\%$ ). 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.

Atmospheric radiation physical process plays an important role in climate simulations. As a radiative transfer scheme, the rapid radiative transfer model for general circulation models (RRTMG) is widely used in weather forecasting and climate simulation systems. However, its expensive computational overhead poses a severe challenge to system performance. Therefore, improving the radiative transfer model’s computational performance has significant scientific research and practical value. Numerous radiative transfer models have benefited from a widely used and powerful GPU. Nevertheless, few of them have exploited CPU/GPU cluster resources within heterogeneous high-performance computing platforms. In this paper, we endeavor to demonstrate an approach that runs a large-scale, computationally intensive, longwave radiative transfer model on a GPU cluster. First, a CUDA-based acceleration algorithm of the RRTMG longwave radiation scheme (RRTMG_LW) on multiple GPUs is proposed. Then, a heterogeneous, hybrid programming paradigm (MPI+CUDA) is presented and utilized with the RRTMG_LW on a GPU cluster. After implementing the algorithm in CUDA Fortran, a multi-GPU version of the RRTMG_LW, namely GPUs-RRTMG_LW, was developed. The experimental results demonstrate that the multi-GPU acceleration algorithm is valid, scalable, and highly efficient when compared to a single GPU or CPU. Running the GPUs-RRTMG_LW on a K20 cluster achieved a $$77.78 \times$$ speedup when compared to a single Intel Xeon E5-2680 CPU core.

Regional climate models (RCMs) are used to dynamically downscale global climate models (GCMs) to provide high-resolution projections of future climate change to better inform policy and decision making at the regional scale. However, biases from GCMs are transferred to RCMs and this can limit the usefulness of the regional climate projections. This paper investigates the influence of bias correcting 4 GCMs from the Coupled Model Intercomparison Project 3, for regional climate simulations over the CORDEX-Australasia domain using Weather Research and Forecasting. The GCM outputs are bias corrected against ERA-Interim reanalysis as a surrogate truth. Results show that over decadal time scales bias correction removes large systematic precipitation and temperature biases. However, bias correction also introduced biases where there were none, introduced biases of the opposite sign, or enhanced existing biases in other regions in some instances. The dynamical mechanisms driving the changes in the biases are explored.

Ocean-Land-Atmosphere Studies (COLA), and NOAA CPO organized a CFSv2 Evaluation Workshop in 2012. This topical collection includes a selection of peer-reviewed papers consisting of material presented at the workshop. The more than 20 papers in this collection have documented significant progress in the performance of CFSv2 in simulating ISI climate variability and predicting key climate variables, in comparison to the previous NCEP operational climate forecast system. The papers have also identified key model biases and deficiencies in predicting climate variables (such as precipitation and temperature), simulating the modes of climate variability and phenomena (such as El Nino and the Southern Oscillation or ENSO, the Madden– Julian Oscillation or MJO, the Arctic Oscillation or AO, global and regional drought, and monsoons) and physical processes and their interactions (such as cloud distributions, land–atmosphere interactions, and ocean–atmosphere interactions). This collection of papers is expected to provide insight into and guidance for the development of the next generation operational CFS. We’d like to express our sincere appreciation to Professor Edwin Schneider for his effort and dedication as the Chief Editor for this special collection. We are pleased to present the Topical Collection of the Climate Forecast System version 2 (CFSv2). The CFS is a coupled global climate model used for operational intraseasonal-to-interannual (ISI) prediction at the National Centers for Environmental Prediction (NCEP). NCEP developed CFSv2 over the course of several years and implemented it into operations in March 2011. External community research supported by the Climate Program Office (CPO) enhanced core internal development and evaluation. To bring the broader climate research and applications community together with NCEP scientists to evaluate the utility of CFSv2 for climate modeling research and as a climate forecast tool, the NCEP Climate Prediction Center (CPC), NOAA Climate Test Bed (CTB), Center for

The sea surface temperature (SST) seasonal cycle in the eastern equatorial Pacific (EEP) plays an important role in the El Nino-Southern Oscillation (ENSO) phenomenon. However, the reasonable simulation of SST seasonal cycle in the EEP is still a challenge for climate models. In this paper, we evaluated the performance of 17 CMIP6 climate models in simulating the seasonal cycle in the EEP and compared them with 43 CMIP5 climate models. In general, only CESM2 and SAM0-UNICON are able to successfully capture the annual mean SST characteristics, and the results showed that CMIP6 models have no fundamental improvement in the model annual mean bias. For the seasonal cycle, 14 out of 17 climate models are able to represent the major characteristics of the observed SST annual evolution. In spring, 12 models capture the 1–2 months leading the eastern equatorial Pacific region 1 (EP1; 5°S–5°N, 110°–85°W) against the eastern equatorial Pacific region 2 (EP2; 5°S–5°N, 140°–110°W). In autumn, only two models, GISS-E2-G and SAM0-UNICON, correctly show that the EP1 and EP2 SSTs vary in phase. For the CMIP6 MME SST simulation in EP1, both the cold bias along the equator in the warm phase and the warm bias in the cold phase lead to a weaker annual SST cycle in the CGCMs, which is similar to the CMIP5 results. However, both the seasonal cold bias and warm bias are considerably decreased for CMIP6, which leads the annual SST cycle to more closely reflect the observation. For the CMIP6 MME SST simulation in EP2, the amplitude is similar to the observed value due to the quasi-constant cold bias throughout the year, although the cold bias is clearly improved after August compared with CMIP5 models. Overall, although SAM0-UNICON successfully captured the seasonal cycle characteristics in the EEP and the improvement from CMIP5 to CMIP6 in simulating EEP SST is clear, the fundamental climate models simulated biases still exist.

Based on a single-model 100-member ensemble of climate change simulations performed with the MPI-ESM, this paper investigates how the probability density function (PDF) of atmospheric general circulation (AGC)—a marginal distribution of the full climate PDF—responds to increasing $${\hbox {CO}}_2$$ concentration. The investigation provides a first assessment of $${\hbox {CO}}_2$$-induced changes in AGC-PDF derived from ensemble statistics (as oppose to time statistics in usual practice) that arise solely from internal variability consistent with the imposed $${\hbox {CO}}_2$$-forcing. By focusing on the first two moments of the AGC-PDF related to the Hadley circulation and the subtropical jets, we find that the major $${\hbox {CO}}_2$$-induced changes in the PDF are related to structure changes in the mean circulation and in the circulation variability. As $${\hbox {CO}}_2$$ concentration increases, different components of the AGC can co-evolve with changing forcing. In particular, the mean Hadley cell co-evolves with the mean subtropical jets. The co-evolution is characterized by the concurrent Hadley cell widening and poleward shifting of the subtropical jets (particularly strong in January), by a strengthening of both the Hadley cell and the subtropical jets in October, and by a weakening of the Hadley cell and a strengthening of the northern and southern jets in January. The changes in circulation variability are more subtle, and reveal only large and spatially coherent changes in places, where the variability is already strong without increasing $${\hbox {CO}}_2$$ concentration, such as at the northern and southern flanks of the summer southern subtropical jet, and at the northern flank of the southern polar night jet. There are some significant cross-variabilities between different components of the AGC. Overall, the $${\hbox {CO}}_2$$-induced AGC-changes can be described by linear trends in the first and second moments, with the trends in the means being much stronger than those in the variances and co-variances.

This paper evaluates the influence that climate change could exert on electricity demand 15patterns in Spain conditioned on the level of warming, with special attention to the 16seasonal occurrence of extreme demand days. For this purpose, assuming the currently 17observed electricity demand–temperature relationship holds in the future, we have gen-18erated daily time series of pseudo-electricity demand for the recent past and the twenty-19first century by using simulated temperatures from statistical downscaling of global 20climate model experiments. We show that both the frequency and severity of extreme 21electricity demand days at the national level are expected to increase, even for low levels 22of regional warming. Moreover, the occurrence of these extremes will experience a 23seasonal shift from winter to summer due to the projected temperature increases in both 24seasons. Under a RCP8.5 scenario of greenhouse gas emissions, the extended summer 25season (June–September) will concentrate more than 50% of extreme electricity demand 26days by the mid-century, increasing to 90% before the end of the century. These changes 27in electricity demand have considerable spatial heterogeneity over the country, with 28northwestern Spain experiencing the seasonal shift later than the rest of the country, 29due to the relatively mild summer temperatures and lower projected warming there.

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.

Made available in DSpace on 2019-10-06T15:25:16Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-08-01 The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with “ground truth” are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp) Campinas State University Federal Institute of Education Science and Technology of Mato Grosso do Sul - Campus of Naviraí School of Agricultural and Veterinarian Sciences São Paulo State University (Unesp)

This paper examines historical and future changes in normalised damages resulting from climate-related natural disasters for the Caribbean. Annualised damages of USD824 million are shown to be non-stationary over the historical period 1964 to 2013. Perturbations of (i) sea surface temperatures (SST) in the tropical North Atlantic (TNA) and (ii) the Atlantic multi-decadal oscillation (AMO) appear to be associated with historical damages. Both the TNA and AMO are known modulators of hurricane activity and rainfall amounts in the Caribbean. Indicative future damages are determined using (i) cumulative distribution functions (CDFs) of perturbed climate states and (ii) an artificial neural network (ANN) model of damages using projected TNA values and the state of the AMO derived from an ensemble of five coupled model intercomparison project phase 5 (CMIP5) global climate models (GCMs) run under the RCP 4.5 scenario. Estimates of future damages are determined when global mean surface temperatures (GMST) reach and exceed 1.5 °C above preindustrial levels. Annual normalised damages may potentially increase to at least USD1395 million or close to double for 1.5 °C. At 2 °C, higher damages may occur; however, large uncertainty across all GCMs prohibits the identification of significant difference between 1.5 and 2 °C. Significant differences in damages do, however, exist for at least two of the GCMs for the two climate states. The robustness of the results is discussed in light of a number of issues, including limitations associated with the data.

The traditional dynamical downscaling (TDD) method employs continuous integration of regional climate models (RCM) with the general circulation model (GCM) providing the initial and lateral boundary conditions. Dynamical downscaling simulations are constrained by physical principles and can generate a full set of climate information, providing one of the important approaches to projecting fine spatial-scale future climate information. However, the systematic biases of climate models often degrade the TDD simulations and hinder the application of dynamical downscaling in the climate-change related studies. New methods developed over past decades improve the performance of dynamical downscaling simulations. These methods can be divided into four groups: the TDD method, the pseudo global warming method, dynamical downscaling with GCM bias corrections, and dynamical downscaling with both GCM and RCM bias corrections. These dynamical downscaling methods are reviewed and compared in this paper. The merits and limitations of each dynamical downscaling method are also discussed. In addition, the challenges and potential directions in progressing dynamical downscaling methods are stated.

This paper aims to validate inflow simulations concerning the present-day climate at Agua Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Agua Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975–2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0–20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate.

Understanding the costs that climate change will exact on society is crucial to devising an appropriate policy response. One of the channels through while climate change will affect human society is through vector-borne diseases whose epidemiology is conditioned by ambient ecology. This paper introduces the literature on malaria, its cost on society, and the consequences of climate change to the physics community in hopes of inspiring synergistic research in the area of climate change and health. It then demonstrates the use of one ecological indicator of malaria suitability to provide an order-of-magnitude assessment of how climate change might affect the malaria burden. The average of Global Circulation Model end-of-century predictions implies a 47% average increase in the basic reproduction number of the disease in today’s malarious areas, significantly complicating malaria elimination efforts.

This paper describes the operational seasonal prediction system of the Japan Meteorological Agency (JMA), the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 1 (JMA/MRI-CPS1), which was in operation at JMA during the period between February 2010 and May 2015. The predictive skill of the system was assessed with a set of retrospective seasonal predictions (reforecasts) covering 30 years (1981–2010). JMA/MRI-CPS1 showed reasonable predictive skill for the El Nino–Southern Oscillation, comparable to the skills of other state-of-the-art systems. The one-tiered approach adopted in JMA/MRI-CPS1 improved its overall predictive skills for atmospheric predictions over those of the two-tiered approach of the previous uncoupled system. For 3-month predictions with a 1-month lead, JMA/MRI-CPS1 showed statistically significant skills in predicting 500-hPa geopotential height and 2-m temperature in East Asia in most seasons; thus, it is capable of providing skillful seasonal predictions for that region. Furthermore, JMA/MRI-CPS1 was superior overall to the previous system for atmospheric predictions with longer (4-month) lead times. In particular, JMA/MRI-CPS1 was much better able to predict the Asian Summer Monsoon than the previous two-tiered system. This enhanced performance was attributed to the system’s ability to represent atmosphere–ocean coupled variability over the Indian Ocean and the western North Pacific from boreal winter to summer following winter El Nino events, which in turn influences the East Asian summer climate through the Pacific–Japan teleconnection pattern. These substantial improvements obtained by using an atmosphere–ocean coupled general circulation model underpin its success in providing more skillful seasonal forecasts on an operational basis.

Climate ensembles utilize outputs from multiple climate models to estimate future climate patterns. These multi-model ensembles generally outperform individual climate models. In this paper, the performance of seven global climate model and regional climate model combinations were evaluated for Ontario, Canada. Two multi-model ensembles were developed and tested, one based on the mean of the seven combinations and the other based on the median of the same seven models. The performance of the multi-model ensembles were evaluated on 12 meteorological stations, as well as for the entire domain of Ontario, using three temperature variables (average surface temperature, maximum surface temperature, and minimum surface temperature). Climate data for developing and validating the multi-model ensembles were collected from three major sources: the North American Coordinated Regional Downscaling Experiment, the Digital Archive of Canadian Climatological Data, and the Climactic Research Unit’s TS v4.00 dataset. The results showed that the climate ensemble based on the mean generally outperformed the one based on the median, as well as each of the individual models. Future predictions under the Representative Concentration Pathway 4.5 (RCP4.5) scenario were generated using the multi-model ensemble based on the mean. This study provides credible and useful information for climate change mitigation and adaption in Ontario.

In this paper, we review the progress made in the studies on general circulation and large-scale dynamics. We divide the advances in the studies on the atmospheric circulation and large-scale dynamics into 4 stages, i.e., the studies on atmospheric wave dynamics; the studies on the observational facts of general circulation; the numerical studies on general circulation; and the studies on planetary wave dynamics, wave-mean flow interaction and nonlinear dynamics. Moreover, the prospects of the studies on the atmospheric circulation and large-scale dynamics for the future are also made in this paper.

It is firmly established in the hydrologic literature that flooding depends on both antecedent watershed wetness and precipitation. One could phrase this relationship as “heavy precipitation does not necessarily lead to high stream discharge”, but rarely do studies directly affirm this statement. We have observed several non-hydrologists mistake trends in heavy precipitation as a proxy for trends in riverine flooding. If the relationship between heavy precipitation and high discharge was more often explicitly presented, heavy precipitation may less often be misinterpreted as a proxy for discharge. In this paper, we undertake such an analysis for 390 watersheds across the contiguous U.S. We found that 99th percentile precipitation only results in 99th percentage discharge 36 % of the time. However, when conditioned on soil moisture from the Variable Infiltration Capacity model, 62 % of 99th percentile precipitation results in 99th percentile discharge during wet periods and only 13 % during dry periods. When relating trends in heavy precipitation to hydrologic response, precipitation data should, therefore, be segregated based on concurrent soil moisture. Taking this approach for climate predictions, we found that CMIP-5 atmosphere–ocean global circulation model (AOGCM) simulations for a RCP 6.0 forcing project increases in concurrence of greater than median soil wetness and extreme precipitation in the northern United States and a decrease in the south, suggesting northern regions could see an increase in very high discharges while southern regions could see decreases despite both regions having an increase in extreme precipitation. While the actual outcome is speculative given the uncertainties of the AOGCM’s, such an analysis provides a more sophisticated framework from which to evaluate the output as well as historic climate data.

Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity (absolute indices and threshold indices), duration (duration indices), and frequency (percentile indices) of extreme temperature. The overall performance of each model is summarized by a “portrait” diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986–2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961–2005.

Since most previous attempts to establish monsoon indices have been limited to specific regions, they have lacked the applicability to universally describe the global monsoon domain. In this paper, we first review the history of global monsoon study and then identify the climatology of global precipitation associated with major systems of the atmospheric general circulation. A new index, based on the annual and semiannual harmonic precipitation rate difference between two local calendar maximal and minimal precipitation pentads, is used to identify the global monsoon domain focusing on where experienced and what caused the climatic dry-wet alteration. The global monsoon domain is defined by the regions where two pentad-mean precipitation difference exceeds 4 mm ⋅day−1, which is also influenced by the low-level prevailing wind reversal associated with the cross-equatorial flow. This definition not only confirmed previous results of the classical global monsoon domain from the tropical Africa to Asia-Australia and non-classical monsoon region in the tropical America but also solved an issue of missing local summer monsoon spots.

This paper is a review of the recent development of researches on the stability of the Atlantic meridional overturning circulation (AMOC). In particular, we will review recent studies that attempt to best assess the stability of the AMOC in the past, present, and future by using a stability indicator related to the freshwater transport by the AMOC. These studies further illustrate a potentially systematic bias in the state-of-the-art atmosphere-ocean general circulation models (AOGCMs), in which the AMOCs seem to be over-stabilized relative to that in the real world. This common model bias in the AMOC stability is contributed, partly, to a common tropical bias associated with the double intertropical convergence zone (ITCZ) in most state-of-the-art AOGCMs, casting doubts on future projection of abrupt climate changes in these climate models.

This paper presents the spatial and temporal structures of drought regimes in Southern Africa and evaluates the capability of ten global climate models (GCMs) in simulating the regimes. The study uses a multi-scaled standardized index (called standardized precipitation evapo-transpiration index, SPEI) in characterizing droughts over Southern Africa at 3- and 12-month scales. The spatial patterns of the drought regimes are identified using the rotated principal component analysis (PCA) on the SPEI, while the temporal characteristics of the drought regimes are studied using wavelet analysis. The relationship between each drought regime and global SSTs (and climate indices) is quantified using correlation analysis and wavelet coherence analysis. The study also quantifies the capability of the GCMs in simulating the drought regimes. The PCA results show four main drought regimes that jointly explain about 50 % SPEI variance over South Africa. The drought regimes (hereafter PF1, PF2, PF3 and PF4) centre over the south-western part of Southern Africa (i.e. South Africa, Botswana and Namibia common border), Zimbabwe, Tanzania, and Angola, respectively. PF1, PF2 and PF4 are strongly correlated with SST over the South Atlantic, Tropical Pacific and Indian Oceans, while PF3 is strongly correlated with the SST over the Tropical Pacific, Atlantic and Indian Oceans. The drought regimes (except PF4) have significant coherence with some atmospheric teleconnection, but the strength, duration, and phase of the coherence vary with time. All the GCMs simulate the drought regimes better at a 3-month scale than at a 12-month scale. At a 3-month scale, 70 % of the GCMs simulate all the drought regimes with a high correlation coefficient (r > 0.6), but at a 12-month scale only 60 % of the models simulate at least three of the drought regimes with a high correlation coefficient (r > 0.6). The results of this study have applications in using GCMs to study the underlying atmospheric dynamics that control droughts and to understand the impacts of global warming on droughts.

The impact of climatic change can be on specific locations. However, the broader the affected area coverage, in mind, the higher would be the chance in missing critical details. In this light, this paper attempts to assess the possible climatic changes and their corresponding implications on agricultural production in northern Ethiopia. The analysis is based on the future (2030 and 2050) temperature and rainfall data, downscaled as ensemble of four general circulation models (GCMs) using the A2 and B1 emission scenarios for ten meteorological stations located in different agroecological zones of the study region. The result indicates that, based on emission scenarios, the mean maximum and minimum temperature would increase by 2–2.3 and 0.8–0.9 °C in 2030 and by 2.2–2.7 and 1.4–1.7 °C in 2050, respectively. This will be accompanied by an increase in the frequency of hot days and nights and a decrease in cool days and nights. While annual rainfall totals will remain unchanged, main rainy season (kiremt) rainfall total would increase on average in 12.9 and 14.2 % under A2 and 9.5 and 11.2 % under B1 by 2030 and 2050, respectively. Owing to an increase in kiremt rainfall, the yield of maize and sorghum may increase at some sites under future climatic conditions, and the increase would be higher under CO2 fertilization. The results suggest the need for site-specific adaptation strategies to reduce the impact and/or exploit the opportunities of climate change.

The present paper uses an atmospheric general circulation model to explore large-scale atmospheric response to various El Nino-Southern Oscillation events associated with tropical cyclone (TC) activity in the western North Pacific. The simulated response is basically consistent with and confirms the observed results. For eastern Pacific warm (EPW) event, anomalously wet ascent occurs over the tropical central/eastern Pacific and dry descent is over the western Pacific. This Walker circulation is associated with anomalous low-level convergence, reduced vertical wind shear (VWS), and enhanced genesis potential index (GPI) in the southeast sub-region. These are consistent with the observed increase of the TC formation in the southeast sub-region but decrease in the northwest sub-region during July–September (JAS) and the increase in the southwest and northwest sub-regions during October–December (OND). In addition, the strong westerly anomalies of the TC steering flow prevail in the East Asian coast, suppressing the TC northwestward or westward tracks. For eastern Pacific cold (EPC) event, all of the simulated variables show almost a mirror image of EPW. For central Pacific warm event, the anomalous Walker circulation shifts westward because of the westward shift of the maximum SST anomaly forcing. The anomalous subsidence associated with the western branch of the Walker circulation during OND shifts northward to the South China Seas, resulting in a decrease of the TC genesis there. The TC steering flow patterns during JAS are favorable for TCs to make landfall over Japan and Korea. Compared with EPC, the descending motion in the central/eastern Pacific is much stronger for central Pacific cold (CPC) event, accompanied by more enhanced VWS and reduced GPI in the southeast sub-region. Therefore, CPC provides a more adverse environment to the TC formation there during JAS and OND, consistent with the observed decrease of TC formation there. Moreover, the easterly anomalies of the TC steering flow dominate the tropics during JAS, enhancing TC activity in the east coast of China. Additionally, the convection over the western Pacific moves to the South China Sea during OND, favoring the TC genesis there.

This paper presents a global scale assessment of the impact of climate change on water scarcity. Patterns of climate change from 21 Global Climate Models (GCMs) under four SRES scenarios are applied to a global hydrological model to estimate water resources across 1339 watersheds. The Water Crowding Index (WCI) and the Water Stress Index (WSI) are used to calculate exposure to increases and decreases in global water scarcity due to climate change. 1.6 (WCI) and 2.4 (WSI) billion people are estimated to be currently living within watersheds exposed to water scarcity. Using the WCI, by 2050 under the A1B scenario, 0.5 to 3.1 billion people are exposed to an increase in water scarcity due to climate change (range across 21 GCMs). This represents a higher upper-estimate than previous assessments because scenarios are constructed from a wider range of GCMs. A substantial proportion of the uncertainty in the global-scale effect of climate change on water scarcity is due to uncertainty in the estimates for South Asia and East Asia. Sensitivity to the WCI and WSI thresholds that define water scarcity can be comparable to the sensitivity to climate change pattern. More of the world will see an increase in exposure to water scarcity than a decrease due to climate change but this is not consistent across all climate change patterns. Additionally, investigation of the effects of a set of prescribed global mean temperature change scenarios show rapid increases in water scarcity due to climate change across many regions of the globe, up to 2 °C, followed by stabilisation to 4 °C.

Gravity waves are primarily generated in the lower atmosphere, and can reach thermospheric heights in the course of their propagation. This paper reviews the recent progress in understanding the role of gravity waves in vertical coupling during sudden stratospheric warmings. Modeling of gravity wave effects is briefly reviewed, and the recent developments in the field are presented. Then, the impact of these waves on the general circulation of the upper atmosphere is outlined. Finally, the role of gravity waves in vertical coupling between the lower and the upper atmosphere is discussed in the context of sudden stratospheric warmings., Accepted for publication in Geoscience Letters

This paper assesses the uncertainties involved in the projections of seasonal temperature and precipitation changes over South America in the twenty-first century. Climate simulations generated by 24 general circulation models are weighted according to the reliability ensemble averaging (REA) approach. The results show that the REA mean temperature change is slightly smaller over South America compared to the simple ensemble mean. Higher reliability in the temperature projections is found over the La Plata basin, and a larger uncertainty range is located in the Amazon. A temperature increase exceeding 2 °C is found to have a very likely (>90 %) probability of occurrence for the entire South American continent in all seasons, and a more likely than not (>50 %) probability of exceeding 4 °C by the end of this century is found over northwest South America, the Amazon Basin, and Northeast Brazil. For precipitation, the projected changes have the same magnitude as the uncertainty range and are comparable to natural variability.

Extreme temperature events are simulated by using the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM) in this paper. The model has been run for 136 yr with the observed external forcing data including solar insolation, greenhouse gases, and monthly sea surface temperature (SST). The daily maximum and minimum temperatures are simulated by the model, and 16 indices representing various extreme temperature events are calculated based on these two variables. The results show that the maximum of daily maximum temperature (TXX), maximum of daily minimum (TNX), minimum of daily maximum (TXN), minimum of daily minimum (TNN), warm days (TX90p), warm nights (TN90p), summer days (SU25), tropical nights (TR20), and warm spell duration index (WSDI) have increasing trends during the 20th century in most regions of the world, while the cold days (TX10p), cold nights (TN10p), and cold spell duration index (CSDI) have decreasing trends.

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.

Statistical downscaling is a technique widely used to overcome the spatial resolution problem of General Circulation Models (GCMs). Nevertheless, the evaluation of uncertainties linked with downscaled temperature and precipitation variables is essential to climate impact studies. This paper shows the potential of a statistical downscaling technique (in this case SDSM) using predictors from three different GCMs (GCGM3, GFDL and MRI) over a highly heterogeneous area in the central Andes. Biases in median and variance are estimated for downscaled temperature and precipitation using robust statistical tests, respectively Mann–Whitney and Brown–Forsythe's tests. In addition, the ability of the downscaled variables to reproduce extreme events is tested using a frequency analysis. Results show that uncertainties in downscaled precipitations are high and that simulated precipitation variables failed to reproduce extreme events accurately. Nevertheless, a greater confidence remains in downscaled temperatures variables for the area. GCMs performed differently for temperature and precipitation as well as for the different test. In general, this study shows that statistical downscaling is able to simulate with accuracy temperature variables. More inhomogeneities are detected for precipitation variables. This first attempt to test uncertainties of statistical downscaling techniques in the heterogeneous arid central Andes contributes therefore to an improvement of the quality of predictions of climate impact studies in this area.

Numerical models of atmosphere–ocean circulation are widely used to understand past climate and to project future climate change. Although the same laws of physics, chemistry, and fluid dynamics govern any general circulation model, each model’s formulations and parameterizations are different, yielding different projections. Notwithstanding, models within an ensemble will have varying degrees of similarity for different outputs of interest. Multi-model ensembles have been used to increase forecast skill by using simple or weighted averages where weights have been obtained by considering factors such as estimated model bias and consensus with other models (Giorgi and Mearns, J. Clim. 15:1141–1158, 2002, Geophys. Res. Lett. 30:1629–1632, 2003; Tebaldi et al., Geophys. Res. Lett. 31:L24213, 2004, J. Clim. 18:1524–1540, 2005). This paper considers an alternative view of multi-model ensembles. For use with the North American Regional Climate Change Assessment Program (NARCCAP), multivariate statistical models are employed to characterize modes of similarity within the members of an ensemble. Specifically, we propose a spatially-correlated latent variable model which facilitates the exploration of when, where, and how regional climate models are similar, and what factors best predict observed locations of model convergence.

How the Hadley circulation changes in response to global climate change and how its change impacts upon regional and global climates has generated a lot of interest in the literature in the past few years. In this paper, consistent and statistically significant poleward expansion of the Hadley circulation in the past few decades is demonstrated, using independent observational datasets as proxy measures of the Hadley circulation. Both observational outgoing longwave radiation and precipitation datasets show an annual average total poleward expansion of the Hadley cells of about 3.6° latitude. Sea level pressure from observational and reanalysis datasets show smaller magnitudes of poleward expansion, of about 1.2° latitude. Ensemble general circulation model simulations forced by observed time-varying sea surface temperatures were found to generate a total poleward expansion of about 1.23° latitude. Possible mechanisms behind the changes in the horizontal extent of the Hadley circulation are discussed.

This paper examines the capability of three regional climate models (RCMs), i.e., RegCM3 (the International Centre for Theoretical Physics Regional Climate Model), PRECIS (Providing Regional Climates for Impacts Studies) and CMM5 (the fifth-generation Pennsylvania State University-the National Center for Atmospheric Research of USA, NCAR Mesoscale Model) to simulate the near-surface-layer winds (10 m above surface) all over China in the late 20th century. Results suggest that like global climate models (GCMs), these RCMs have the certain capability of imitating the distribution of mean wind speed and fail to simulate the greatly weakening wind trends for the past 50 years in the country. However, RCMs especially RegCM3 have the better capability than that of GCMs to simulate the distribution and change feature of mean wind speed. In view of their merits, these RCMs were used to project the variability of near-surface-layer winds over China for the 21st century. The results show that 1) summer mean wind speed for 2020–2029 will be lower compared to those in 1990–1999 in most area of China; 2) annual and winter mean wind speed for 2081–2100 will be lower than those of 1971–1990 in the whole China; and 3) the changes of summer mean wind speed for 2081–2100 are uncertain. As a result, although climate models are absolutely necessary for projecting climate change to come, there are great uncertainties in projections, especially for wind speed, and these issues need to be further explored.

Vertical cumulus momentum transport is an important physical process in the tropical atmosphere and plays a key role in the evolution of the tropical atmospheric system. This paper focuses on the impact of the vertical cumulus momentum transport on Madden-Julian Oscillation (MJO) simulation in two global climate models (GCMs). The Tiedtke cumulus parameterization scheme is applied to both GCMs [CAM2 and Spectral Atmospheric general circulation Model of LASG/IAP (SAMIL)]. It is found that the MJO simulation ability might be influenced by the vertical cumulus momentum transport through the cumulus parameterization scheme. However, the use of vertical momentum transport in different models provides different results. In order to improve model’s MJO simulation ability, we must introduce vertical cumulus momentum transport in a more reasonable way into models. Furthermore, the coherence of the parameterization and the underlying model also need to be considered.

The papers in this Special Issue are the primary technical underpinnings for the Northeast Climate Impacts Assessment (NECIA), an integrated regional-scale assessment of projected climate change, impacts and options for mitigation and adaptation across the US Northeast. The consequences of future pathways of greenhouse gas emissions on projected climate and impacts across climate-sensitive sectors is assessed by using downscaled projections from three global climate models under both higher (Alfi) and lower (B1) emissions scenarios. The findings illustrate that near-term reductions in emissions can greatly reduce the extent and severity of regionally important impacts on natural and managed ecosystems and public health in the latter half of this century, and increase the feasibility that those impacts which are now unavoidable can be successfully managed through adaptation.

In order to effectively improve numerical prediction level by using current models and data, the strategy and methodology of dynamical analogue prediction (DAP) is deeply studied in the present paper. A new idea to predict the prediction errors of dynamical model on the basis of historical analogue information is put forward so as to transform the dynamical prediction problem into the estimation problem of prediction errors. In terms of such an idea, a new prediction method of final analogue correction of errors (FACE) is developed. Furthermore, the FACE is applied to extra-seasonal prediction experiments on an operational atmosphere-ocean coupled general circulation model. Prediction results of summer mean circulation and total precipitation show that the FACE can to some extent reduce prediction errors, recover prediction variances, and improve prediction skills. Besides, sensitive experiments also show that predictions based on the FACE are evidently influenced by the number of analogues, analogue-selected variables and analogy metric.

An overview of the PRUDENCE fine resolution climate model experiments for Europe is presented in terms of their climate change signals, in particular 2-meter temperature and precipitation. A comparison is made with regard to the seasonal variation in climate change response of the different models participating in the project. In particular, it will be possible to check how representative a particular PRUDENCE regional experiment is of the overall set in terms of seasonal values of temperature and precipitation. This is of relevance for such further studies and impact models that for practical reasons cannot use all the PRUDENCE regional experiments. This paper also provides some guidelines for how to select subsets of the PRUDENCE regional experiments according to such main sources of uncertainty in regional climate simulations as the choice of the emission scenario and of the driving global climate model.

This paper examines the performance of an atmospheric general circulation model (AGCM) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics (LASG/IAP). It is a spectral model truncated at R42(2.8125°long×1.66°lat) resolution and with nine vertical levels, and referred to as R42L9/LASG hereafter. It is also the new version of atmospheric component model R15L9 of the global ocean-atmosphere-land system (GOALS/LASG). A 40-year simulation in which the model is forced with the climatological monthly mean sea surface temperature is compared with the 40-year (1958–97) U.S. National Center for Environmental Prediction (NCEP) global reanalysis and the 22-year (1979–2000) Xie-Arkin monthly precipitation climatology. The mean DJF and JJA geographical distributions of precipitation, sea level pressure, 500-hPa geopotential height, 850-hPa and 200-hPa zonal wind, and other fields averaged for the last 30-year integration of the R42L9 model are analyzed. Results show that the model reproduces well the observed basic patterns, particularly precipitation over the East Asian region. Comparing the new model with R15L9/LASG, the old version with coarse resolution (nearly 7.5°long×4.5°lat), shows an obvious improvement in the simulation of regional climate, especially precipitation. The weaknesses in simulation and future improvements of the model are also discussed.

Based on the Kalman filter theory, a new data-assimilation method has been used to improve the 3-D oceanic temperature field of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled general circulation model. This method is applied to assimilate surface and subsurface temperature of in situ measurements from the Pilot Research Moored Array in the Tropical Atlantic project (PIRATA). The assimilation of the PIRATA data produces an improved representation of the thermal state of the ocean and allows a better estimation of other oceanographic quantities, like meridional heat fluxes and zonal currents. The present paper focuses on the tropical Atlantic and, in particular, it contains new reconstructed temperature profiles. One-month forecast experiments during 1999 were performed and the impact of the assimilation is discussed.

This paper presents a numerical study on the 1998 summer rainfall over the Yangtze River valley in central and eastern China, addressing effect of a nested area size on simulations in terms of the technique of nesting a regional climate model (RCM) upon a general circulation model (GCM). Evidence suggests that the size exerts greater impacts upon regional climate of the country, revealing that a larger nested size is superior to a small one for simulation in mitigating errors of GCM-provided lateral boundary forcing. Also, simulations show that the RCM should incorporate regions of climate systems of great importance into study and a low-resolution GCM yields more pronounced errors as a rule when used in the research of the Tibetan Plateau, and, in contrast, our PσRCM can do a good job in describing the plateau’s role in a more realistic and accurate way. It is for this reason that the tableland should be included in the nested area when the RCM is employed to investigate the regional climate. Our PσRCM nesting upon a GCM reaches more realistic results compared to a single GCM used.

This paper reviews recent progress and problems in modeling the thermohaline circulation of the world ocean by use of z-coordinate ocean general circulation models. Discussions focus on four issues: sea surface forcing, mixing in the deep ocean interior, eddy-induced tracer transport, and bottom boundary layer processes. Although some widely used techniques and parameterizations deal with these issues, some aspects are still overlooked and more sophistication is certainly required.

Anthropogenic climate change is a relatively new phenomenon, largely occurring over the past 150 years, and much of the discussion on climate change impacts to forests has focused on long-term shifts in temperature and precipitation. However, individual trees respond to the much shorter impacts of climate variability. Historically, fast growing, fully canopied, non-chronically stressed (NCS) trees (e.g., those with minimal insect or disease damage, growing on high nutrient, moist soils), have been considered the model of individual health, while slower growing, broken crown, chronically stressed (CS) trees growing on nutrient poor, or dry soils, were considered to be unhealthy and prone to stress related mortality. Over the coming years and decades, climate variability (e.g., extreme precipitation events, drought, heat waves) is expected to increase. We hypothesize that as the duration, severity and interaction of stress becomes more pronounced under increasing climate variability, seemingly less healthy, CS trees could begin to survive environmental stress better than their traditionally considered healthy, NCS tree counterparts. We further hypothesize that this shift in survivorship will be attributed to tree response to increasingly extreme climate variability at levels not previously observed in recorded history. While NCS trees can better survive historic episodic stress, we hypothesize that CS trees may be better suited to survive the combination of chronic low-level stress, and extreme episodic stress associated with anthropogenic climate change and increasing climate variability. Specifically, morphological differences such as increased root and reduced leaf mass, and physiological differences such as more oleoresin production, could make slower growing, CS forests more resilient to catastrophic episodic mortality compared to more NCS stands under certain circumstances. General circulation models suggest that climate variability will increase in the future. Therefore, the occurrence of slow growth, CS forest stands out surviving faster growing NCS less stressed stands may become more common place, and eventually lead to “The Age of the Mediocre Forest”. This paper addresses the type of environmental conditions that could create such an inverse in forest health conditions, and the implications that this shift might have in forest genetics and management.