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This paper investigates the impact and potential use of the cut cell vertical discretisation for forecasts of 5 days and climate simulations. A first indication of the usefulness of this new method is obtained by a set of five-day forecasts, covering January 1989 by forecasts. The model area was chosen to include much of Asia, the Himalayas and Australia. The cut cell model LMZ provides a much more accurate representation of mountains on model forecasts than the terrain following coordinate used for comparison. Therefore we are in particular interested in potential forecast improvements in the target area downwind of the Himalaya, over South East China, Korea and Japan. The LMZ has been tested so far extensively for one-day forecasts on an European area. Following indications of a reduced temperature error for the short forecasts, this paper investigates the model error for five days in an area influenced by strong orography. The forecasts indicated a strong impact of the cut cell discretisation on forecast quality. The cut cell model is available only of an older (2003) Version of the model LM. It was compared using a control model differing by the use of the terrain following coordinate only. The cut cell model improved the precipitation forecasts of this old control model everywhere by a large margin. An improved version of the terrain following model LM has been developed since then under the name CLM. The CLM has been used and tested in all climates, while the LM was used for small areas in higher latitudes. The precipitation forecasts of cut cell model were compared also to the CLM. As the cut cell model LMZ did not incorporate the developments for CLM since 2003, the precipitation forecast of the CLM was not improved in all aspects. However, for the target area downstream of the Himalaya, the cut cell model improved the prediction of the monthly precipitation forecast even in comparison with the modern model version CLM considerably. The cut cell discretisation seems to improve in particular the localisation of precipitation, while the improvements leading from LM to CLM had a positive effect mainly on amplitude. [ABSTRACT FROM AUTHOR]

We evaluate the isotopic composition of water vapor and precipitation simulated by the LMDZ GCM over Siberia using several datasets: TES and GOSAT satellite observations of tropospheric water vapor, GNIP and SNIP precipitation networks, and daily, in-situ measurements of water vapor and precipitation at the Kourovka site in Western Siberia. We use dD vs. humidity diagrams to explore the complementarity of these two variables to interpret model biases in terms of the representation of processes. LMDZ captures the spatial, seasonal and daily variations reasonably well. It systematically overestimates dD in the vapor and precipitation, a bias that is most likely associated with a misrepresentation of air mass origin. The performance of LMDZ is put in the context of other isotopic models from the SWING2 models. There is significant spread among models in the simulation of dD, and of the dD vs. humidity relationship. This confirms that dD brings additional information compared to humidity only. We specifically investigate the added value of water isotopic measurements to interpret the warm and dry bias feature by most GCMs over mid and high latitude continents in summer. LMDZ simulates the strongest dry bias on days when it simulates the strongest enriched bias in dD. The analysis of the slopes in dD vs. humidity diagrams and of processes controlling dD and humidity variations suggests that the cause of the moist bias could be either a problem in the large-scale advection transporting too much dry and warm air from the south, or insufficient surface evaporation. [ABSTRACT FROM AUTHOR]

The article presents a study that demonstrates the usefulness of the Norwegian Climate Center's Earth System Model (NorESM1-M) in the coupled model intercomparison project phase 5 (CMIP5). It outlines the role of the model in providing complementary results to the evaluation of possible man made climate change. It also demonstrates the effectiveness of the model in accurately depicting the changes in the atmospheric water cycle in precipitation events.

We use the process-based VS (Vaganov-Shashkin) model to investigate whether a regional Pinus halapensis tree-ring chronology from Tunisia can be simulated as a function of climate alone by employing a biological model linking day length and daily temperature and precipitation (AD 1959-2004) from a climate station to ring-width variations. We use two periods to calibrate (1982-2004) and verify (1959-1981) the model. We have obtained highly significant positive correlation between the residual chronology and estimated growth curve (r = 0.76 p < 0.001). The model shows that the average duration of the growing season is 191 days. On average, soil moisture limits tree-ring growth for 128 days and temperature for 63 days. [ABSTRACT FROM AUTHOR]

Spatial information on inundation dynamics is expected to improve greenhouse gas estimates in climate models. Satellite data can provide land cover information from local to global scale. The detection capability for dynamics is however limited. Cloud cover and daylight independent methods are required for frequent updates. Suitable are therefore sensors which make use of microwaves. The purpose of the present study is to assess such data for determination of wetland dynamics from the viewpoint of use in climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR) operating in C-band due to, among microwave systems, comparably good spatial resolution and data availability. Continuity is also expected for such systems. Simple classification algorithms can be applied to detect open water in an automatised way allowing the processing of time series. Such approaches are robust when the water surface is smooth. C-band data from ENVISAT ASAR (Advanced SAR) operating in wide swath mode (150m resolution) have been investigated for implementation of an automated detection procedure of open water fraction. More than 4000 samples (single acquisitions tiled into 0.5 degree grid cells) have been analysed for July/August 2007 and 2008. Modification of input parameters results in differences below 1% open water fraction. The actual challenge is the frequent occurrence of waves due to wind and precipitation. This reduces the separability of the water class from other land cover. The possible update intervals for surface water extent are therefore decreased considerably. Statistical measures of the backscatter distribution can be applied in order to retrieve the for classification suitable data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution has been used for assessment. On average only 40% of acquisitions allow a separation of the open water class. Satellite data are available every 2-3 days over the Western Siberian study region. With respect to the irregular acquisition intervals and varying length of unsuitable weather periods a minimum update interval of 10 days is suggested for the Northern Eurasian test case. Although SAR data availability is currently constraint future satellite missions which aim for operational services such as Sentinel-1 with its C-band SAR instrument may provide the basis for inundation monitoring in support of climate modelling. [ABSTRACT FROM AUTHOR]

Tropical tree height-diameter (H:D) relationships may vary by forest type and region making large-scale estimates of above-ground biomass subject to bias if they ignore these differences in stem allometry. We have therefore developed a new global tropical forest database consisting of 39 955 concurrent H and D measurements encompassing 283 sites in 22 tropical countries. Utilising this database, our objectives were: 1. to determine if H:D relationships differ by geographic region and forest type (wet to dry forests, including zones of tension where forest and savanna overlap). 2. to ascertain if the H:D relationship is modulated by climate and/or forest structural characteristics (e.g. stand-level basal area, A). 3. to develop H:D allometric equations and evaluate biases to reduce error in future local-to-global estimates of tropical forest biomass. Annual precipitation coefficient of variation (PV), dry season length (SD), and mean annual air temperature (TA) emerged as key drivers of variation in H:D relationships at the pantropical and region scales. Vegetation structure also played a role with trees in forests of a high A being, on average, taller at any given D. After the effects of environment and forest structure are taken into account, two main regional groups can be identified. Forests in Asia, Africa and the Guyana Shield all have, on average, similar H:D relationships, but with trees in the forests of much of the Amazon Basin and tropical Australia typically being shorter at any given D than their counterparts elsewhere. The region-environment-structure model with the lowest Akaike's information criterion and lowest deviation estimated stand-level H across all plots to within a median -2.7 to 0.9% of the true value. Some of the plot-to-plot variability in H:D relationships not accounted for by this model could be attributed to variations in soil physical conditions. Other things being equal, trees tend to be more slender in the absence of soil physical constraints, especially at smaller D. Pantropical and continental-level models provided only poor estimates of H, especially when the roles of climate and stand structure in modulating H:D allometry were not simultaneously taken into account. [ABSTRACT FROM AUTHOR]

This paper shows that joint temperature-precipitation information over a global domain provides a more accurate estimate of aerosol forced responses in climate models than does any other combination of temperature, precipitation, or sea level pressure. This fact is demonstrated using a new quantity called potential detectability, which measures the extent to which a forced response can be detected in a model. In particular, this measure can be evaluated independently of observations and therefore permits efficient exploration of a large number of variable combinations before performing optimal fingerprinting on observations. This paper also shows that the response to anthropogenic aerosol forcing can be separated from that of other forcings using only spatial structure alone, leaving the time variation of the response to be inferred from data, thereby demonstrating that temporal information is not necessary for detection. The spatial structure of the forced response is derived by maximizing the signal-to-noise ratio. For single variables, the north-south hemispheric gradient and equator-to-pole latitudinal gradient are important spatial structures for detecting anthropogenic aerosols in some models but not all. Sea level pressure is not an independent detection variable because it is derived partly from surface temperature. In no case does sea level pressure significantly enhance potential detectability beyond that already possible using surface temperature. Including seasonal or land-sea contrast information does not significantly enhance detectability of anthropogenic aerosol responses relative to annual means over global domains. [ABSTRACT FROM AUTHOR]

The objective of this paper is to identify better performing Coupled Model Intercomparison Project phase 3 (CMIP3) global climate models (GCMs) that reproduce grid-scale climatological statistics of observed precipitation and temperature for input to hydrologic simulation over global land regions. Current assessments are aimed mainly at examining the performance of GCMs from a climatology perspective and not from a hydrology standpoint. The performance of each GCM in reproducing the precipitation and temperature statistics was ranked and better performing GCMs identified for later analyses. Observed global land surface precipitation and temperature data were drawn from the Climatic Research Unit (CRU) 3.10 gridded data set and re-sampled to the resolution of each GCM for comparison. Observed and GCM-based estimates of mean and standard deviation of annual precipitation, mean annual temperature, mean monthly precipitation and temperature and Köppen-Geiger climate type were compared. The main metrics for assessing GCM performance were the Nash-Sutcliffe efficiency (NSE) index and root mean square error (RMSE) between modelled and observed long-term statistics. This information combined with a literature review of the performance of the CMIP3 models identified the following better performing GCMs from a hydrologic perspective: HadCM3 (Hadley Centre for Climate Prediction and Research), MIROCm (Model for Interdisciplinary Research on Climate) (Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change), MIUB (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), MPI (Max Planck Institute for Meteorology) and MRI (Japan Meteorological Research Institute). The future response of these GCMs was found to be representative of the 44 GCM ensemble members which confirms that the selected GCMs are reasonably representative of the range of future GCM projections. [ABSTRACT FROM AUTHOR]

This paper examines the performance of three climate re-analysis datasets in Angola, namely the ERA-40, NCEP-r1 and JRA-55, by comparing annual and seasonal estimates of precipitation, surface air temperature and evaporation with ground observation measurements. The observational dataset describes a region poorly covered by international climate databases and it is believed that most of its data have not been used in the data assimilation procedures of the climate models. This paper therefore provides a fresh look at the performance of these climate re-analysis datasets in a vast area where distance and civil war have hindered ground monitoring efforts. The re-analysis exercises offer better temperature estimates than precipitation estimates. When offered, the evaporation estimates from all three products are very poor. The three products are able to describe the main features of the spatial distribution of average annual precipitation and temperature, but struggle to reproduce the temporal changes of these variables. The results from a set of performance criteria show that the correlation between the observed ground measurements and re-analysis estimates is poor overall and the NSE values indicate that the average measured value at each location is usually a better estimate than the re-analysis estimate.EDITOR A. CastellarinASSOCIATE EDITOR S. Kanae [ABSTRACT FROM AUTHOR]

Simulations from a regional climate model (RCM) as part of a superensemble experiment were compared with observations of surface meteorological variables over the western United States. The RCM is the Hadley Centre Regional Climate Model, version 3, with improved physics parameterizations (HadRM3P) run at 25-km resolution and nested within the Hadley Centre Atmosphere Model, version 3 (HadAM3P). Overall, the means of seasonal temperature were well represented in the simulations; 95% of grid points were within 2.7°, 2.4°, and 3.6°C of observations in winter, spring, and summer, respectively. The model was too warm over most of the domain in summer except central California and southern Nevada. HadRM3P produced more extreme temperatures than observed. The overall magnitude and spatial pattern of precipitation were well characterized, though HadRM3P exaggerated the orographic enhancement along the coastal mountains, Cascade Range, and Sierra Nevada. HadRM3P produced warm/dry northwest, cool/wet southwest U.S. patterns associated with El Niño. However, there were notable differences, including the locations of the transition from warm (dry) to cool (wet) in the anomaly fields when compared with observations, though there was disagreement among observations. HadRM3P simulated the observed spatial pattern of mean annual temperature more faithfully than any of the RCM-GCM pairings in the North American Regional Climate Change Assessment Program (NARCCAP). Errors in mean annual precipitation from HadRM3P fell within the range of errors of the NARCCAP models. Last, this paper provided examples of the size of an ensemble required to detect changes at the local level and demonstrated the effect of parameter perturbation on regional precipitation. [ABSTRACT FROM AUTHOR]

State-of-the-art hydrological climate impact assessment involves ensemble approaches to address uncertainties. For precipitation, a wide range of climate model runs is available. However, for particular meteorological variables used for the calculation of potential evapotranspiration (ETo), availability of climate model runs is limited. It is preferred that climate model runs are considered coupled when calculating changes in precipitation and ETo amounts, in order to preserve the internal physical consistency. This results in constraints on the maximum ensemble size. In this paper, we investigate the correlation between climate change signals of precipitation and ETo. It is found that, for two medium-sized catchments in Belgium, uncoupling climate model runs used for calculation of change signals of precipitation and ETo amounts does not result in a significant bias for changes in extreme flow. With these results, future impact studies can be conducted with larger ensemble sizes, resulting in a more complete uncertainty estimation. [ABSTRACT FROM AUTHOR]

Decadal climate predictability has received considerable scientific interest in recent years, yet the limits and mechanisms for decadal predictability are currently not well known. It is widely accepted that noise due to internal atmospheric dynamics at the air‐sea interface influences predictability. The purpose of this paper is to use the interactive ensemble (IE) coupling strategy to quantify how internal atmospheric noise at the air‐sea interface impacts decadal predictability. The IE technique can significantly reduce internal atmospheric noise and has proven useful in assessing seasonal‐to‐interannual variability and predictability. Here we focus on decadal timescales and apply the nonlinear local Lyapunov exponent method to the Community Climate System Model comparing control simulations with IE simulations. This is the first time the nonlinear local Lyapunov exponent has been applied to the state‐of‐the‐art coupled models. The global patterns of decadal predictability are discussed from the perspective of internal atmospheric noise and ocean dynamics. Key Points: For the first time, the nonlinear local Lyapunov exponent method has been applied to the state‐of‐the‐art coupled climate modelsDepending on region reducing internal atmospheric noise reduces decadal variability but either increases or decreases decadal predictabilityDecadal‐scale subsurface predictability is examined for the North Atlantic region [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]

Global climate models project widespread decreases in soil moisture over large parts of Europe. This paper investigates the impact of model resolution on the magnitude and seasonality of future soil drying in central-western Europe. We use the general circulation model EC-Earth to study two 30-year periods representative of the start and end of the 21st century under low-to-moderate greenhouse gas forcing (RCP4.5). In our study area, central-western Europe, at high spatial resolution (∼25 km) soil drying is more severe and starts earlier in the season than at standard resolution (∼112 km). Here, changes in the large-scale atmospheric circulation and local soil moisture feedbacks lead to enhanced evapotranspiration in spring and reduced precipitation in summer. A more realistic position of the storm track at high model resolution leads to reduced biases in precipitation and temperature in the present-day climatology, which act to amplify future changes in evapotranspiration in spring. Furthermore, in the high-resolution model a stronger anticyclonic anomaly over the British Isles extends over central-western Europe and supports soil drying. The resulting drier future land induces stronger soil moisture feedbacks that amplify drying conditions in summer. In addition, soil-moisture-limited evapotranspiration in summer promotes sensible heating of the boundary layer, which leads to a lower relative humidity with less cloudy conditions, an increase in dry summer days, and more incoming solar radiation. As a result a series of consecutive hot and dry summers appears in the future high-resolution climate. The enhanced drying at high spatial resolution suggests that future projections of central-western European soil drying by CMIP5 models have been potentially underestimated. Whether these results are robust has to be tested with other global climate models with similar high spatial resolutions. [ABSTRACT FROM AUTHOR]

Serbia is situated at Balkan Peninsula, and currently majority of the territory is under warm temperate - fully humid climate type with warm summers (Cfb type, according to Koppen-Geiger Climate Classification). Observed changes in climate conditions since 1961 until present time show significant increase in temperature change and change in precipitation patterns. Disturbances in heat conditions, which are recorded to affect human health, agricultural production and forest ecosystem, are priority in climate change analysis and application in adaptation planning. Future change analysis show accelerated increase of temperature by the end of the 21st century, which proves the needs for immediate measures for mitigation of negative impacts. Temperature increase averaged over the territory of Serbia is 1.2 °C for the period 1996-2015 with respect to the period 1961-1980, with highest increase of maximum daily temperature during the summer season, 2.2 °C. Using high resolution multi-model ensemble approach for analysis of the future changes with respect to the base period 1986-2005, in compliance with Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5), it is estimated that temperature may increase by 1.9 °C according to Representative Concentration Pathway 4.5 (RCP4.5) scenario and by 4.4 °C according to RCP8.5 by the end of the century. Spatial distribution of temperature increase, intensification of high precipitation events and decrease of summer precipitation, show intrusion of subtropical climate over the Serbia and increase of high temperature and high precipitation risks. Results presented in this paper, using high-resolution multi-model ensemble approach, provide climate change information for short term to long term planning in different sectors of economy and preservation of human health and environment. [ABSTRACT FROM AUTHOR]

The time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms). [ABSTRACT FROM AUTHOR]

The Nanjing University of Information Science and Technology Earth System Model version 3 (NESM v3) has been developed, aiming to provide a numerical modeling platform for cross-disciplinary Earth system studies, project future Earth climate and environment changes, and conduct subseasonal-to-seasonal prediction. While the previous model version NESM v1 simulates the internal modes of climate variability well, it has no vegetation dynamics and suffers considerable radiative energy imbalance at the top of the atmosphere and surface, resulting in large biases in the global mean surface air temperature, which limits its utility to simulate past and project future climate changes. The NESM v3 has upgraded atmospheric and land surface model components and improved physical parameterization and conservation of coupling variables. Here we describe the new version's basic features and how the major improvements were made. We demonstrate the v3 model's fidelity and suitability to address global climate variability and change issues. The 500-year preindustrial (PI) experiment shows negligible trends in the net heat flux at the top of atmosphere and the Earth surface. Consistently, the simulated global mean surface air temperature, land surface temperature, and sea surface temperature (SST) are all in a quasi-equilibrium state. The conservation of global water is demonstrated by the stable evolution of the global mean precipitation, sea surface salinity (SSS), and sea water salinity. The sea ice extents (SIEs), as a major indication of highlatitude climate, also maintain a balanced state. The simulated spatial patterns of the energy states, SST, precipitation, and SSS fields are realistic, but the model suffers from a cold bias in the North Atlantic, a warm bias in the Southern Ocean, and associated deficient Antarctic sea ice area, as well as a delicate sign of the double ITCZ syndrome. The estimated radiative forcing of quadrupling carbon dioxide is about 7.24Wm-2, yielding a climate sensitivity feedback parameter of -0:98Wm-2 K-1, and the equilibrium climate sensitivity is 3.69 K. The transient climate response from the 1%yr-1 CO2 (1pctCO2) increase experiment is 2.16 K. The model's performance on internal modes and responses to external forcing during the historical period will be documented in an accompanying paper. [ABSTRACT FROM AUTHOR]

'Neighborhood approaches' have been used in two primary ways to postprocess and verify high-resolution ensemble output. While the two methods appear deceptively similar, they define events over different spatial scales and yield fields with different interpretations: the first produces probabilities interpreted as likelihood of event occurrence at the grid scale, while the second produces probabilities of event occurrence over spatial scales larger than the grid scale. Unfortunately, some studies have confused the two methods, while others did not acknowledge multiple possibilities of neighborhood approach application and simply stated, 'a neighborhood approach was applied' without supporting details. Thus, this paper reviews applications of neighborhood approaches to convection-allowing ensembles in hopes of clarifying the two methods and their different event definitions. Then, using real data, it is demonstrated how the two approaches can yield statistically significantly different objective conclusions about model performance, underscoring the critical need for thorough descriptions of how neighborhood approaches are implemented and events are defined. The authors conclude by providing some recommendations for application of neighborhood approaches to convection-allowing ensembles. [ABSTRACT FROM AUTHOR]

Pattern scaling is a well-established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare two methods of pattern scaling for annual mean precipitation (regression and epoch difference) and evaluate which method is "better" in particular circumstances by quantifying their robustness to interpolation/extrapolation in time, inter-model variations, and inter-scenario variations. Both the regression and epoch-difference methods (the two most commonly used methods of pattern scaling) have good absolute performance in reconstructing the climate model output, measured as an area-weighted root mean square error. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion. Extrapolating RCP8.5 patterns to reconstruct precipitation change in the RCP2.6 scenario results in large errors due to violations of pattern scaling assumptions when this CO2-/non-CO2-forcing decomposition is applied. The methodologies discussed in this paper can help provide precipitation fields to be utilized in other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change. [ABSTRACT FROM AUTHOR]

Human-induced land-use change (LUC) alters the biogeophysical characteristics of the land surface influencing the surface energy balance. The level of atmospheric CO2 is expected to increase in the coming century and beyond, modifying temperature and precipitation patterns and altering the distribution and physiology of natural vegetation. It is important to constrain how CO2-induced climate and vegetation change may influence the regional extent to which LUC alters climate. This sensitivity study uses the HadCM3 coupled climate model under a range of equilibrium forcings to show that the impact of LUC declines under increasing atmospheric CO2, specifically in temperate and boreal regions. A surface energy balance analysis is used to diagnose how these changes occur. In Northern Hemisphere winter this pattern is attributed in part to the decline in winter snow cover and in the summer due to a reduction in latent cooling with higher levels of CO2. The CO2-induced change in natural vegetation distribution is also shown to play a significant role. Simulations run at elevated CO2, yet present-day vegetation show a significantly increased sensitivity to LUC, driven in part by an increase in latent cooling. This study shows that modeling the impact of LUC needs to accurately simulate CO2-driven changes in precipitation and snowfall and incorporate accurate, dynamic vegetation distribution. [ABSTRACT FROM AUTHOR]

This paper clarifies that the skillful time-scale characteristic of a model is one of the key factors to reproduce the amount precipitation at a specific location with the model. A comparison with data from an operational weather station of the Japan Meteorological Agency in Tokyo (Ote-machi) revealed that a model needed That a model requires 5-km-grid resolution and below to represent the power spectrum of hourly precipitation. A model with a higher resolution is probably needed to simulate hourly precipitation in Tokyo during the summer monsoon season. [ABSTRACT FROM AUTHOR]

The need to draw climate-related inferences from historical data makes understanding the biases and errors in these data critical. While climate data are collected at point-level monitoring sites, they are often postprocessed by averaging sites within a geographic area to align the data to a grid, easing analysis and visualization. Although this aggregation generally provides reasonable estimates of the mean, its use can be problematic for characterizing the full distribution of climate measures. Specifically, the process of averaging point-level data up to grid level can lead to inconsistencies, particularly when the grid box is heterogeneous and extremes are of interest. Point-level data are measured at individual points, while gridded data are the averaged product of many measurements within a larger spatial area. Because of this aggregation, point-level and grid-level distributions differ in many fundamental properties, such as their shape, skew, and tail behavior. This paper highlights these differences and their effects on analyses pertaining to current climatological questions. Mathematical relationships are derived to link the distributions of grid-level climate measures to the distributions of point-level climate measures using the notion of effective sample size. Then, these relationships are leveraged to propose a correction factor to use when modeling higher moments and extreme events. [ABSTRACT FROM AUTHOR]

Results from a regional climate model simulation show substantial increases in future flood risk (2040-69) in many Pacific Northwest river basins in the early fall. Two primary causes are identified: 1) more extreme and earlier storms and 2) warming temperatures that shift precipitation from snow to rain dominance over regional terrain. The simulations also show a wide range of uncertainty among different basins stemming from localized storm characteristics. While previous research using statistical downscaling suggests that many areas in the Pacific Northwest are likely to experience substantial increases in flooding in response to global climate change, these initial estimates do not adequately represent the effects of changes in heavy precipitation. Unlike statistical downscaling techniques applied to global climate model scenarios, the regional model provides an explicit, physically based simulation of the seasonality, size, location, and intensity of historical and future extreme storms, including atmospheric rivers. This paper presents climate projections from the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model dynamically downscaled using the Weather Research and Forecasting (WRF) Model implemented at 12-km resolution for the period 1970-2069. The resulting daily precipitation and temperature data are bias corrected and used as input to a physically based Variable Infiltration Capacity (VIC) hydrologic model. From the daily time step simulations of streamflow produced by the hydrologic model, probability distributions are fit to the extreme events extracted from each water year and flood statistics for various return intervals are estimated. [ABSTRACT FROM AUTHOR]

This paper discusses the relative performance of several climate models in providing reliable forcing for hydrological modeling in six representative catchments in the Mediterranean region. We consider 14 Regional Climate Models (RCMs), from the EU-FP6 ENSEMBLES project, run for the A1B emission scenario on a common 0.22º (about 24 km) rotated grid over Europe and the Mediterranean region. In the validation period (1951 to 2010) we consider daily precipitation and surface temperatures from the observed data fields (E-OBS) data set, available from the ENSEMBLES project and the data providers in the ECA&D project. Our primary objective is to rank the 14 RCMs for each catchment and select the four best-performing ones to use as common forcing for hydrological models in the six Mediterranean basins considered in the EU-FP7 CLIMB project. Using a common suite of four RCMs for all studied catchments reduces the (epistemic) uncertainty when evaluating trends and climate change impacts in the 21st century. We present and discuss the validation setting, as well as the obtained results and, in some detail, the difficulties we experienced when processing the data. In doing so we also provide useful information and advice for researchers not directly involved in climate modeling, but interested in the use of climate model outputs for hydrological modeling and, more generally, climate change impact studies in the Mediterranean region. [ABSTRACT FROM AUTHOR]

The NCAR Community Earth System Model (CESM) now includes an atmospheric component that extends in altitude to the lower thermosphere. This atmospheric model, known as the Whole Atmosphere Community Climate Model (WACCM), includes fully interactive chemistry, allowing, for example, a self-consistent representation of the development and recovery of the stratospheric ozone hole and its effect on the troposphere. This paper focuses on analysis of an ensemble of transient simulations using CESM1(WACCM), covering the period from the preindustrial era to present day, conducted as part of phase 5 of the Coupled Model Intercomparison Project. Variability in the stratosphere, such as that associated with stratospheric sudden warmings and the development of the ozone hole, is in good agreement with observations. The signals of these phenomena propagate into the troposphere, influencing near-surface winds, precipitation rates, and the extent of sea ice. In comparison of tropospheric climate change predictions with those from a version of CESM that does not fully resolve the stratosphere, the global-mean temperature trends are indistinguishable. However, systematic differences do exist in other climate variables, particularly in the extratropics. The magnitude of the difference can be as large as the climate change response itself. This indicates that the representation of stratosphere-troposphere coupling could be a major source of uncertainty in climate change projections in CESM. [ABSTRACT FROM AUTHOR]

Soil moisture is an essential climate variable (ECV) of major importance for land-atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observation-based soil moisture dataset has become available that provides information on soil moisture dynamics from satellite observations (ECVSM, essential climate variable soil moisture). The present study investigates the potential and limitations of this new dataset for several applications in climate model evaluation. We compare soil moisture data from satellite observations, reanalysis and simulations from a state-of-the-art land surface model and analyze relationships between soil moisture and precipitation anomalies in the different dataset. Other potential applications like model parameter optimization or model initialization are not investigated in the present study. In a detailed regional study, we show that ECVSM is capable to capture well the interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies. [ABSTRACT FROM AUTHOR]

The rapid expansion of irrigation in the Great Plains since World War II has resulted in significant water table declines, threatening the long-term sustainability of the Ogallala Aquifer. As discussed in Part I of this paper, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation at subgrid scales. Simulations of nine April-October periods (three drought, three normal, and three pluvial) over the Great Plains were completed to assess the full impact of irrigation on the water budget. Averaged over all simulated years, irrigation over the Great Plains contributes to May-September evapotranspiration increases of approximately 4% and precipitation increases of 1%, with localized increases of up to 20%. Results from these WRF simulations are used along with a backward trajectory analysis to identify where evapotranspiration from irrigated fields falls as precipitation (i.e., irrigation-induced precipitation) and how irrigation impacts precipitation recycling. On average, only 15.8% of evapotranspiration from irrigated fields falls as precipitation over the Great Plains, resulting in 5.11 mm of May-September irrigation-induced precipitation and contributing to 6.71 mm of recycled precipitation. Reductions in nonrecycled precipitation suggest that irrigation reduces precipitation of moisture advected into the region. The heaviest irrigation-induced precipitation is coincident with simulated and observed precipitation increases, suggesting that observed precipitation increases in north-central Nebraska are strongly related to evapotranspiration of irrigated water. Water losses due to evapotranspiration are much larger than irrigation-induced precipitation and recycled precipitation increases, confirming that irrigation results in net water loss over the Great Plains. [ABSTRACT FROM AUTHOR]

Many of the processes that govern precipitation occurrence and intensity occur at spatial and temporal scales that remain unresolved by the current generation of climate models. The resulting mismatch between the information reliably provided by such models and the needs of researchers assessing impacts associated with changes in precipitation has led to the development of downscaling techniques. In this paper, the need for downscaling is demonstrated by describing (1) the difference between real-world precipitation-governing processes and their representation in climate models and (2) the differences between theoretical expectations of precipitation changes at large and small scales in a warmer world. With a primary focus on empirical-statistical downscaling, the general strategy for developing regional scenarios is described along with outstanding issues that are likely to shape the next decade of downscaling research. [ABSTRACT FROM AUTHOR]

Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of the user community and are now available with 0.25° × 0.25° (or less) subdaily resolutions. This paper evaluates a number of commonly available satellite-derived HRPPs covering northwest Europe over a 6-yr period. Precipitation products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), the Climate Prediction Center (CPC) morphing (CMORPH) technique, the CPC merged microwave technique, the Naval Research Laboratory (NRL) blended technique, and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) technique. In addition, the Geosynchronous Operational Environmental Satellite (GOES) precipitation index (GPI) and the European Centre for Medium-Range Weather Forecasting (ECMWF) operational forecast model products are included for comparison. Surface reference data from the European radar network is used as ground truth, supported by the Global Precipitation Climatology Centre (GPCC) precipitation gauge analysis and gauge data over the United Kingdom. Measures of correlation, bias ratio, probability of detection, and false alarm ratio are used to evaluate the products. Results show that satellite products generally exhibit a seasonal cycle in correlation, bias ratio, probability of detection, and false alarm ratio, with poorer statistics during the winter. The ECMWF model also shows a seasonal cycle in the correlation, although the results are poorer during the summer, while the bias ratio, probability of detection, and false alarm ratio are consistent through all seasons. Importantly, all the satellite HRPPs underestimate precipitation over northwest Europe in all seasons. [ABSTRACT FROM AUTHOR]

Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP's stage IV analysis were used to estimate the model parameters (2002-04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated. [ABSTRACT FROM AUTHOR]

This paper presents a validation study for a high-resolution version of the Regional Climate Model version 3 (RegCM3) over the Carpathian basin and its surroundings. The horizontal grid spacing of the model is 10 km--the highest reached by RegCM3. The ability of the model to capture temporal and spatial variability of temperature and precipitation over the region of interest is evaluated using metrics spanning a wide range of temporal (daily to climatology) and spatial (inner domain average to local) scales against different observational datasets. The simulated period is 1961--90. RegCM3 shows small temperature biases but a general overestimation of precipitation, especially in winter; although, this overestimate may be artificially enhanced by uncertainties in observations. The precipitation bias over the Hungarian territory, the authors'' main area of interest, is mostly less than 20%%. The model captures well the observed late twentieth-century decadal-to-interannual and interseasonal variability. On short time scales, simulated daily temperature and precipitation show a high correlation with observations, with a correlation coefficient of 0.9 for temperature and 0.6 for precipitation. Comparison with two Hungarian station time series shows that the model performance does not degrade when going to the 10-km gridpoint scale. Finally, the model reproduces the spatial distribution of dry and wet spells over the region. Overall, it is assessed that this high-resolution version of RegCM3 is of sufficiently good quality to perform climate change experiments over the Carpathian region--and, in particular, the Hungarian territory--for application to impact and adaptation studies. [ABSTRACT FROM AUTHOR]

Overlying the challenge of managing within natural hydroclimatic variability is the likely modification of runoff variability along with average runoff due to anthropogenic enhancement of greenhouse gas concentrations. In this paper analytical models are developed in which runoff mean and variability, the latter defined by the variance (or standard deviation) of annual runoff, are related to the variances and the covariance of annual precipitation and potential evapotranspiration, and the aridity index (mean annual potential evapotranspiration divided by mean annual precipitation). The method was validated using observed runoff data for 699 worldwide catchments. It was concluded that combining the Schreiber function, which relates the ratio of annual actual evapotranspiration to annual precipitation, with the analytical models provided satisfactory estimates of observed annual runoff mean and interannual variability. It was also concluded that estimates of annual runoff variability based on the simplified model of Koster and Suarez were unsatisfactory. By way of illustrating the new methodology, the approach was applied to projected annual values of precipitation from the Hadley Centre Global Environment Model version 1 (HadGEM) and it showed that considerable changes in reservoir yield are likely to occur if climate change projections of precipitation from HadGEM are realistic. Finally, further simplifications of the equations, based on the Schreiber function, are developed to estimate the mean and standard deviation of annual runoff that allow climate analysts to estimate the impact of potential climate changes on annual runoff characteristics and reservoir yield performance without having to resort to the calibration and application of a rainfall-runoff model or rely on the runoff output from general circulation models to examine such characteristics. [ABSTRACT FROM AUTHOR]

Reliable projections of future changes in local precipitation extremes are essential for informing policy decisions regarding mitigation and adaptation to climate change. In this paper, the extent to which the natural variability of the climate affects one’s ability to project the anthropogenically forced component of change in daily precipitation extremes across Europe is examined. A three-member ensemble of the Hadley Centre Regional Climate Model (HadRM3H) is used and a statistical framework is applied to estimate the uncertainty due to the full spectrum of climate variability. In particular, the results and understanding presented here suggest that annual to multidecadal natural variability may contribute significant uncertainty. For this ensemble projection, extreme precipitation changes at the grid-box level are found to be discernible above climate noise over much of northern and central Europe in winter, and parts of northern and southern Europe in summer. The ability to quantify the change to a reasonable level of accuracy is largely limited to regions in northern Europe. In general, where climate noise has a significant component varying on decadal time scales, single 30-yr climate change projections are insufficient to infer changes in the extreme tail of the underlying precipitation distribution. In this context, the need for ensembles of integrations is demonstrated and the relative effectiveness of spatial pooling and averaging for generating robust signals of extreme precipitation change is also explored. The key conclusions are expected to apply more generally to other models and forcing scenarios. [ABSTRACT FROM AUTHOR]

Pattern scaling is a well established method for approximating modeled spatial distributions of changes in temperature by assuming a time-invariant pattern that scales with changes in global mean temperature. We compare three methods of pattern scaling for precipitation (regression, epoch difference, and a physically-based method) and evaluate which methods are "better" in particular circumstances by quantifying their robustness to interpolation/extrapolation, inter-model variations, and inter-scenario variations. Although the regression and epoch difference methods (the two most commonly used methods of pattern scaling) have better absolute performance in reconstructing the climate model output by two orders of magnitude (measured as an area-weighted root mean square error), the physically-based method shows a greater degree of robustness (less relative root-mean-square variation than the other two methods) and could be a particularly advantageous method if outstanding biases could be reduced. We decompose the precipitation response in the RCP8.5 scenario into a CO2 portion and a non-CO2 portion; these two patterns oppose each other in sign. Due to low signal-to-noise ratios, extrapolating RCP8.5 patterns to re-construct precipitation change in the RCP2.6 scenario results in double the error of reconstructing the RCP8.5 scenario for the regression and epoch difference methods. The methodologies discussed in this paper can help provide precipitation fields for other models (including integrated assessment models or impacts assessment models) for a wide variety of scenarios of future climate change. [ABSTRACT FROM AUTHOR]

The present study utilizes three high-resolution simulations from the Regional Climate Model version 4 (RegCM4) to examine the late 21st century changes (2080–2099) in the West African Monsoon (WAM) features. A set of three Earth System Models are utilized to provide initial and lateral boundary conditions to the RegCM4 experiments. Our analysis focuses on seasonal mean changes in WAM large-scale dynamical features, along with their connections with the summer monsoon precipitation. In the historical period, the simulation ensemble means mimic reasonably well the intensity and spatial distribution of the WAM rainfall as well as the WAM circulation patterns at different scales. The future projection of the WAM climate exhibits warming over the whole West Africa leading to precipitation reduction over the Sahel region, and a slight increase over some areas of the Guinea Coast. The position of the African Easterly Jet (AEJ) is shifted southward and the African Easterly Waves (AEWs) activities are reduced, which affect in turn the WAM rainbelt characteristics in terms of position and strength. Overall the changes in simulated AEJ and AEWs contribute substantially to reduce the seasonal summer mean precipitation in West Africa by the late 21st century, with prevailing negative changes in the Savanna-Sahel region. To further explore the robustness of the relationships revealed in this paper, future studies using different high-resolution regional climate models with large ensemble are recommended. [ABSTRACT FROM AUTHOR]

Changes in extreme precipitation are critical to assess the potential impacts of climate change on human and natural systems. This paper provides a comprehensive investigation on the multi-scale temporal variability of extreme precipitation in the Source Region of the Yellow River (SRYR). The statistical analysis explores multi-scale extreme precipitation variability ranging from short to long term, including seasonal, annual, and inter-annual variations at different locations in the SRYR. The results suggest that seasonal patterns of extreme precipitation do not always follow the seasonal pattern of total precipitation. Heavy precipitation mostly happens during the period from May and October with July as the peak, while dry conditions are mostly seen in winter seasons. However, there are no significant annual trends for most indices at most locations. The extreme heavy precipitation presents an increasing trend at high elevation and decreasing trend at low elevation. The extreme dry condition presents more consistently decreasing trends at nearly all locations. Long-term analyses indicate that most of the selected indices except average daily intensity display multi-year bands ranging from 2 to 8 years which is probably due to the effects of El Niño–Southern Oscillation (ENSO). A further evaluation on how the ENSO events would impact extreme precipitation shows that eastern Pacific warming (EPW) and central Pacific warming (CPW) would bring less extreme heavy precipitation compared to normal years. These results can provide a beneficial reference to understand the temporal variability of extreme precipitation in the SRYR. [ABSTRACT FROM AUTHOR]

This paper analyses current precipitation rates (PRs) and trends over the Mediterranean Sea region and their response to global climate change scenarios. The analysis uses 0.25° gridded PRs dataset over a 13-year period (1998-2010) based on remote sensing data from the Tropical Rainfall Measuring Mission. Future scenarios use the results of six global climate models (GCMs) under four representative concentration pathway scenarios (i.e., RCP26, RCP45, RCP60, and RCP85). Results indicate that the Mediterranean Sea region displays a seasonally significant (insignificant) wetter trend during cold (hot) seasons, and exhibits annual spatial variation ranging from under 15 to over 100 mm month -1 over the period 1998-2010. Sea level pressure has two different effects on precipitation over the northern (inversely related to precipitation) versus southern (directly related to precipitation) Mediterranean Sea. However, sea surface temperature is anti-correlated with precipitation. The GCMs that describe the current Mediterranean Sea precipitation most realistically are GFDL-CM3-1, MIROC-ESM-CHEM, and HadGEM2-AO, which are used to calculate the ensemble mean for each representative concentration pathway scenario. The ensemble means realizations indicate that the study area will experience substantial drought in the 21st century. Uncertainty in the projected precipitation over the Mediterranean Sea was partitioned into four sources, of which the used scenario dominates. [ABSTRACT FROM AUTHOR]

This paper analyses the present Egyptian Mediterranean coast (EMC) climate and the response of its climate variables to global changes. First, the accuracy of the ERA-Interim dataset (1979-2010) for the studied region is examined by comparing these data with available independent observations. Second, the qualities of six global climate models (GCMs), together with the ensemble mean of multiple model realisations of the A1B scenario, are examined by comparing these with the ERA-Interim dataset. Finally, GCM simulations are used to describe the uncertainties in future climate change along the EMC. The results indicate that the observations are in good agreement with the ERA-Interim data. The data for the EMC, 1979-2000, display a significant positive trend for 2-m air temperature together with significant negative trends for total precipitation and sea level pressure. The climate model that best describes the present EMC climate is the CGCM 3.1 model, which is used to describe the future climate of the study area. The CGCM 3.1 model indicates that the EMC area will experience significant warming, substantial droughts, and a weak decrease in sea level pressure in the end of the current century. [ABSTRACT FROM AUTHOR]

In China, summer precipitation contributes a major part of the total precipitation amount in a year and has major impacts on society and human life. Whether any changes in summer precipitation are affected by external forcing on the climate system is an important issue. In this study, an optimal fingerprinting method was used to compare the observed changes of total, heavy, moderate, and light precipitation in summer derived from newly homogenized observation data with the simulations from multiple climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). The results demonstrate that the anthropogenic forcing signal can be detected and separated from the natural forcing signal in the observed increase of seasonal accumulated precipitation amount for heavy precipitation in summer in China and eastern China (EC). The simulated changes in heavy precipitation are generally consistent with observed change in China but are underestimated in EC. When the changes in precipitation of different intensities are considered simultaneously, the human influence on simultaneous changes in moderate and light precipitation can be detected in China and EC in summer. Changes attributable to anthropogenic forcing explain most of the observed regional changes for all categories of summer precipitation, and natural forcing contributes little. In the future, with increasing anthropogenic influence, the attribution-constrained projection suggests that heavy precipitation in summer will increase more than that from the model raw outputs. Society may therefore face a higher risk of heavy precipitation in the future. [ABSTRACT FROM AUTHOR]

Over the past decades, significant changes in temperature and precipitation have been observed, including changes in the mean and extremes. It is critical to understand the trends in hydroclimatic extremes and how they may change in the future as they pose substantial threats to society through impacts on agricultural production, economic losses, and human casualties. In this study, we analyzed projected changes in the characteristics, including frequency, seasonal timing, and maximum spatial and temporal extent, as well as severity, of extreme temperature and precipitation events, using the severity–area–duration (SAD) method and based on a suite of 37 climate models archived in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Comparison between the CMIP5 model estimated extreme events and an observation-based dataset [Princeton Global Forcing (PGF)] indicates that climate models have moderate success in reproducing historical statistics of extreme events. Results from the twenty-first-century projections suggest that, on top of the rapid warming indicated by a significant increase in mean temperature, there is an overall wetting trend in the Northern Hemisphere with increasing wet extremes and decreasing dry extremes, whereas the Southern Hemisphere will have more intense wet extremes. The timing of extreme precipitation events will change at different spatial scales, with the largest change occurring in southern Asia. The probability of concurrent dry/hot and wet/hot extremes is projected to increase under both RCP4.5 and RCP8.5 scenarios, whereas little change is detected in the probability of concurrent dry/cold events and only a slight decrease of the joint probability of wet/cold extremes is expected in the future. [ABSTRACT FROM AUTHOR]

The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu, produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low‐level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low‐level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective. [ABSTRACT FROM AUTHOR]

Using a high-spatial- and high-temporal-resolution precipitation dataset, Integrated Multi-satellite Retrievals for GPM (IMERG), extratropical cyclone precipitation is evaluated in two reanalyses and two climate models. Based on cyclone-centered composites, all four models overestimate precipitation in the western subsiding and dry side of the cyclones, and underestimate the precipitation in the eastern ascending and moist side. By decomposing the composites into frequency of occurrence and intensity (mean precipitation rate when precipitating), the analysis reveals a tendency for all four models to overestimate frequency and underestimate intensity, with the former issue dominating in the western half and the latter in the eastern half of the cyclones. Differences in frequency are strongly dependent on cyclone environmental moisture, while the differences in intensity are strongly impacted by the strength of ascent within the cyclone. There are some uncertainties associated with the observations: IMERG might underreport frozen precipitation and possibly exaggerate rates in vigorously ascending regions. Nevertheless, the analysis suggests that all models produce extratropical cyclone precipitation too often and too lightly. These biases have consequences when evaluating the changes in precipitation characteristics with changes in cyclone properties: the models disagree on the magnitude of the change in precipitation intensity with a change in environmental moisture and in precipitation frequency with a change in cyclone strength. This complicates accurate predictions of precipitation changes in a changing climate. [ABSTRACT FROM AUTHOR]

Wintertime midlatitude cyclone activity and precipitation are projected to increase across northern Europe and decrease over southern Europe, particularly over the western Mediterranean. Greater confidence in these regional projections may be established by their replication in state-of-the-art, high-resolution global climate models that resolve synoptic-scale dynamics. We evaluated the representation of the wintertime eddy-driven and subtropical jet streams, extratropical cyclone activity, and precipitation across the North Atlantic and Europe under historical (1985–2011) and RCP8.5 sea surface temperature forcing in an ensemble of atmosphere-only HadGEM3-GA3.0 simulations, where horizontal atmospheric resolution is increased from 135 to 25 km. Under RCP8.5, increased (decreased) frequency of northern (southern) eddy-driven jet occurrences and a basinwide poleward shift in the upper-level westerly flow are simulated. Increasing atmospheric resolution significantly enhances these climate change responses. At 25-km resolution, these enhanced changes in large-scale circulation amplify increases (decreases) in extratropical cyclone track density and mean intensity across the northern (southern) Euro-Atlantic region under RCP8.5. These synoptic changes with resolution impact the overall climate change response of mean and heavy winter precipitation: wetter (drier) conditions in northern (southern) Europe are also amplified at 25-km resolution. For example, the reduction in heavy precipitation simulated over the Iberian Peninsula under RCP8.5 is ~15% at 135 km but ~30% at 25-km resolution. Conversely, a shift to more frequent high extratropical cyclone (ETC)-associated precipitation rates is simulated over Scandinavia under RCP8.5, which is enhanced at 25 km. This study provides evidence that global atmospheric resolution may be a crucial consideration in European winter climate change projections. [ABSTRACT FROM AUTHOR]

Representing the West African Monsoon is a major challenge in climate modeling because of complex mechanisms of interaction across scales. We study the monsoon precipitation response to a 4K increase in sea surface temperatures using an idealized meridional‐vertical (2D) model, which allows separating regional‐ and larger‐scale influences. The 2D simulations reproduce key features of Atmospheric Model Intercomparison Project simulations and indicate that, in response to sea surface temperature increase, large‐scale changes induce a decrease of Sahel precipitation, while regional‐scale mechanisms generate a southward shift of the rainband. Large‐scale changes moisten and warm the free troposphere, while regional circulation changes increase low‐level moisture. Precipitation in the control simulations accounts for much of the variance in precipitation change for both Atmospheric Model Intercomparison Project and 2D simulations. This response is strongly connected to the large‐scale drivers of temperature and moisture, with an additional spread associated with the formulation of the convective parameterization. Plain Language Summary: The Sahel region of West Africa receives most of its annual rainfall during the monsoon season. Projections of monsoon precipitation are important to the region's growing population, yet climate models disagree on the magnitude and direction of precipitation change in future climates. This study uses a simplified model of the West African monsoon, which we show behaves similarly to complex global climate simulations. Our model allows us to disentangle monsoon changes due to directly changing sea surface temperatures from changes due to the larger‐scale response to increased temperatures. We find that both play important roles in precipitation change, the direct forcing shifts the precipitation, while the large‐scale forcing produces drying. Processes that occur on small spatial scales play an important role in producing precipitation. We investigate how a few of these are represented in our model and discover that the change in precipitation is mostly dependent on how the large‐scale forcing produces precipitation in the current climate, with a spread that is connected to how these physical processes are represented. Our study shows the usefulness of a simplified monsoon model and increases understanding of how increasing sea surface temperatures alter the West African monsoon. Key Points: A 2D monsoon model produces similar changes to general circulation models in monsoon features when sea surface temperatures are increasedSouthward shift of the rainband is connected to regional forcing, while Sahel precipitation decrease is mainly driven by larger‐scale changePrecipitation change is strongly linked to large‐scale drivers of temperature and moisture with additional spread from parameterizations [ABSTRACT FROM AUTHOR]

A full analysis of 3-month Standardized Precipitation-Evapotranspiration index (SPEI-3) changes and attribution analyses are of significance for deeply understanding dryness/wetness evolutions and thus formulating specific measures to sustain regional development. In this study, we analyze monthly and annual SPEI-3 changes over Southwest China (SWC; including Sichuan (SC), Chongqing (CQ), Guizhou (GZ), Yunnan (YN), and west Guangxi (wGX)) during 1961–2012, using the SPEI model and routine meteorological measurements at 269 weather sites. For SWC and each subregion (excluding wGX), annual SPEI-3 during 1961–2012 tends to decrease, and drying is at most of months in January and September–December, but wetting is in February–August (excluding March for wGX). Additionally, more than 50% of sites show declined and increased SPEI-3 in January, April, June, and August–December and the remaining months, respectively. Except for wGX with dominant of ET0, annual SPEI-3 changes in SWC and other four subregions have dominant of precipitation. Spatially, annual SPEI-3 changes at 59% of sites are because of precipitation, generally located in southeast SC, south YN, CQ, GZ, and south and northeast wGX. Nevertheless, dominants at regional and site scales vary among months, e.g., SWC, SC, CQ, and GZ, having dominant of precipitation (ET0) during September–December (most of months during January–August), YN always with dominant of precipitation, and wGX with dominant of precipitation (ET0) in February–April and July–December (January, May, and June). Importantly, this study provides a reference for quantitatively evaluating spatiotemporal dryness/wetness variations with climate change, especially for regions with significant drying/wetting. [ABSTRACT FROM AUTHOR]

As communities prepare for the impacts of climate change, policy makers and stakeholders increasingly require locally resolved projections of future climate. Statistical downscaling uses low‐resolution outputs from climate models and historical observations to both enhance the spatial resolution and correct for systematic biases. By examining the downscaled rainfall over land, we show that although bias corrections are effective in reducing biases in the current climate, they do not reduce the intermodel spread in future rainfall projections. This failure stems from the strong dependence of future rainfall change upon the current climatological rainfall patterns. Even after bias corrections are applied, the downscaled projections of precipitation change retain this dependence upon their native climatology. However, we show that this dependence can be exploited; even very simple methods to subset models according to their ability to resolve the observed rainfall climatology can substantially reduce the intermodel spread in rainfall projections. Plain Language Summary: To prepare for future climate change, policy makers and stakeholders require reliable model projections with high spatial resolution. To meet this need, statistical methods have been developed to postprocess the model output so correct for systematic biases and enhance the spatial resolution. We focus on rainfall over land and find that the postprocessing only yields consistent values for the current climate and no reduction in the uncertainty is obtained for future projections even after the postprocessing. We show that patterns of future rainfall change exhibit a strong dependence on the current climate. This dependence can be exploited by selecting models based on their ability to reproduce the observed climate. We show that this screening of models can significantly reduce the uncertainty in future rainfall projections. The success of this simple method emphasizes the importance of model evaluation in reducing the uncertainty of multimodel climate change projections. Key Points: Statistical downscaling improves the current climatology but does not reduce the intermodel spread of future rainfall projectionsModels with more similar base climates yield more similar projections of change both with and without statistical downscalingScreening models provides a simple method for constraining future projections and is insensitive to the choice of observational data sets [ABSTRACT FROM AUTHOR]

Hydrologic intensity is often quantified using precipitation without directly incorporating atmospheric water demand. We develop a hydrologic intensity index called the surplus deficit intensity (SDI) index that accounts for variation in supply and demand. SDI is the standardized sum of standardized surplus intensity (mean of daily surplus when supply > demand) and deficit time (mean of consecutive days when demand > supply). Using an observational ensemble of global daily precipitation and atmospheric water demand during 1979–2017, we document widespread hydrologic intensification (SDI; +0.11 z‐score per decade) driven primarily by increased surplus intensity. Using a climate model ensemble of the United States, hydrologic intensification is projected for the mid‐21st century (+0.86 in z‐score compared to 1971–2000), producing greater apparent intensification when compared to an index that does not explicitly incorporate demand. While incorporating demand had a minor effect on observed hydrologic intensification, it doubles hydrological intensification for the mid‐21st century. Plain Language Summary: Increasing air temperatures have resulted in an intensification, or acceleration, of the terrestrial hydrologic cycle, which is defined as an increase of the water fluxes (precipitation and evapotranspiration) between the surface and the atmosphere. Efforts to quantify hydrologic intensification have traditionally only considered "supply" variables such as precipitation rates without considering "demand" that varies over space and time. For this work we develop a method to quantify hydrologic intensification using supply and demand. This approach shows widespread hydrologic intensification from 1979–2017 across much of the global land surface, which is expected to continue into the future. We additionally compare this new method to a previously developed method that uses only supply without explicitly incorporating demand. Incorporating demand results in a notable hydrologic intensification compared to methods that only use supply or a simplified representation of supply and demand. This work suggests that demand needs to be considered when quantifying hydrologic intensification. Key Points: We develop a hydrologic intensity metric that incorporates daily supply and demand using precipitation and reference evapotranspirationWidespread hydrologic intensification occurred over the past four decades and will continue into the futureAdding demand couples intensity metrics to ecohydrologic conditions and produces greater increases than metrics with static demand [ABSTRACT FROM AUTHOR]

Climate models project that extreme precipitation events will intensify in proportion to their intensity during the 21st century at large spatial scales. The identification of the causes of this phenomenon nevertheless remains tenuous. Using a large ensemble of North American regional climate simulations, we show that the more rapid intensification of more extreme events also appears as a robust feature at finer regional scales. The larger increases in more extreme events than in less extreme events are found to be primarily due to atmospheric circulation changes. Thermodynamically induced changes have relatively uniform effects across extreme events and regions. In contrast, circulation changes weaken moderate events over western interior regions of North America and enhance them elsewhere. The weakening effect decreases and even reverses for more extreme events, whereas there is further intensification over other parts of North America, creating an "intense gets intenser" pattern over most of the continent. Plain Language Summary: Climate models project that extreme precipitation events will intensify during the 21st century at large spatial scales, with several studies suggesting that the most extreme events will exhibit the highest rate of intensification. Identification of the causes of this phenomenon nevertheless remains tenuous, partly because estimating long‐term changes in precipitation extremes is difficult, particularly for precipitation extremes at impact‐relevant spatial scales. Robustly estimated changes in precipitation extremes at small spatial scales can only be obtained from large extreme precipitation data sets from large ensemble simulations. We employ a large ensemble regional climate simulation experiment performed for North America. The large volume of output from this experiment allows us to confidently obtain statistical evidence that precipitation intensification occurs more rapidly with warming for more extreme events at impact‐relevant spatial scales, and secondly, to determine the causes for this phenomenon. The effect of atmospheric moisture increases caused by greenhouse gas warming is found to be similar for extreme precipitation events of different intensities, ranging from 2‐ to 50‐year events. In contrast, atmospheric circulation change due to greenhouse gas warming tends to reduce the effect of the atmospheric moisture increases on less intense events rather than intensifying the effect on more extreme events. Key Points: More rapid increases are found in more extreme precipitation events in response to warmingThe more rapid increases in more extreme events are caused by atmospheric circulation changesThermodynamically induced changes have relatively uniform effects across extreme events [ABSTRACT FROM AUTHOR]

State-of-the-art general circulation models show important systematic errors in their simulation of sea surface temperatures (SST), especially in the tropical Atlantic. In this work the spread in the simulation of climatological SST in the tropical Atlantic by 24 CMIP5 models is examined, and its relationship with the mean systematic biases in the region is explored. The modes of intermodel variability are estimated by applying principal component (PC) analysis to the SSTs in the region 70°W–20°E, 20°S–20°N. The intermodel variability is approximately explained by the first three modes. The first mode is related to warmer SSTs in the basin, shows worldwide connections with same-signed loads over most of the tropics, and is connected with lower low cloud cover over the eastern parts of the subtropical oceans. The second mode is restricted to the Atlantic, where it shows negative and positive loads to the north and south of the equator, respectively, and is connected to a too weak Atlantic meridional overturning circulation (AMOC). The third mode is related to the double intertropical convergence zone bias in the Pacific and to an interhemispheric asymmetry in the net radiation at the top of the atmosphere. The structure of the second mode is closer to the mean bias than that of the others in the tropical Atlantic, suggesting that model difficulties with the AMOC contribute to the regional biases. [ABSTRACT FROM AUTHOR]