Regional Climate Projections Research Articles

South Asia river-flow projections and their implications for water resources

Abstract. South Asia is a region with a large and rising population, a high dependence on water intense industries, such as agriculture and a highly variable climate. In recent years, fears over the changing Asian summer monsoon (ASM) and rapidly retreating glaciers together with increasing demands for water resources have caused concern over the reliability of water resources and the potential impact on intensely irrigated crops in this region. Despite these concerns, there is a lack of climate simulations with a high enough resolution to capture the complex orography, and water resource analysis is limited by a lack of observations of the water cycle for the region. In this paper we present the first 25 km resolution regional climate projections of river flow for the South Asia region. Two global climate models (GCMs), which represent the ASM reasonably well are downscaled (1960–2100) using a regional climate model (RCM). In the absence of robust observations, ERA-Interim reanalysis is also downscaled providing a constrained estimate of the water balance for the region for comparison against the GCMs (1990–2006). The RCM river flow is routed using a river-routing model to allow analysis of present-day and future river flows through comparison with available river gauge observations. We examine how useful these simulations are for understanding potential changes in water resources for the South Asia region. In general the downscaled GCMs capture the seasonality of the river flows but overestimate the maximum river flows compared to the observations probably due to a positive rainfall bias and a lack of abstraction in the model. The simulations suggest an increasing trend in annual mean river flows for some of the river gauges in this analysis, in some cases almost doubling by the end of the century. The future maximum river-flow rates still occur during the ASM period, with a magnitude in some cases, greater than the present-day natural variability. Increases in river flow could mean additional water resources for irrigation, the largest usage of water in this region, but has implications in terms of inundation risk. These projected increases could be more than countered by changes in demand due to depleted groundwater, increases in domestic use or expansion of water intense industries. Including missing hydrological processes in the model would make these projections more robust but could also change the sign of the projections.

Top ten European heatwaves since 1950 and their occurrence in the coming decades

The Russian heatwave in 2010 killed tens of thousands of people, and was by far the worst event in Europe since at least 1950, according to recent studies and a novel universal heatwave index capturing both the duration and magnitude of heatwaves. Here, by taking an improved version of this index, namely the heat wave magnitude index daily, we rank the top ten European heatwaves that occurred in the period 1950–2014, and show the spatial distribution of the magnitude of the most recent heatwave in summer 2015. We demonstrate that all these events had a strong impact reported in historical newspapers. We further reveal that the 1972 heatwave in Finland had a comparable spatial extent and magnitude as the European heatwave of 2003, considered the second strongest heatwave of the observational era. In the next two decades (2021–2040), regional climate projections suggest that Europe experiences an enhanced probability for heatwaves comparable to or greater than the magnitude, extent and duration of the Russian heatwave in 2010. We demonstrate that the probability of experiencing a major European heatwave in the coming decades is higher in RCP8.5 than RCP4.5 even though global mean temperature projections do not differ substantially. This calls for a proactive vulnerability assessment in Europe in support of formulating heatwave adaptation strategies to reduce the adverse impacts of heatwaves.

Impact of land-use and land-cover changes on CRCM5 climate projections over North America for the twenty-first century

The aim of this study is to assess the impact of land-use and land-cover change (LULCC) on regional climate projections for North America. To this end, two transient climate change simulations, with and without LULCC, but identical atmospheric forcing, are performed with the 5th generation of the Canadian Regional Climate Model (CRCM5) driven by CanESM2 model for the (2006–2100)-RCP4.5 scenario. For the simulation with LULCC, land-cover data sets are taken from the Global Change Assessment Model representing the RCP4.5 scenario for the period 2006–2100. LULCC in RCP4.5 scenario point to significant reduction in cultivated land (e.g. Canadian Prairies and Mississippi basin) due to intense afforestation. Results suggest that biogeophysical effects of LULCC on climate, assessed through differences between the all-forcing (atmospheric and LULCC) run and the atmospheric forcing run (with constant land cover) are substantial for relevant surface variables. It is shown that the afforestation of cropland lead to warmer regional climates, especially in winter (warming above 1.5 °C), as compared with climates resulting from atmospheric forcings alone. The investigation of processes leading to this response shows high sensitivity of the results to changes in albedo as a response to LULCC. Additional roughness, evaporative cooling and water soil availability also seem to play an important role in regional climate especially for the summer season in certain afforested areas (e.g., southeastern US).

Physical Mechanisms of Tropical Climate Feedbacks Investigated Using Temperature and Moisture Trends*

Abstract Tropical climate feedback mechanisms are assessed using satellite-observed and model-simulated trends in tropical tropospheric temperature from the MSU/AMSU instruments and upper-tropospheric humidity from the HIRS instruments. Despite discrepancies in the rates of tropospheric warming between observations and models, both are consistent with constant relative humidity over the period 1979–2008. Because uncertainties in satellite-observed tropical-mean trends preclude a constraint on tropical-mean trends in models regional features of the feedbacks are also explored. The regional pattern of the lapse rate feedback is primarily determined by the regional pattern of surface temperature changes, as tropical atmospheric warming is relatively horizontally uniform. The regional pattern of the water vapor feedback is influenced by the regional pattern of precipitation changes, with variations of 1–2 W m−2 K−1 across the tropics (compared to a tropical-mean feedback magnitude of 3.3–4 W m−2 K−1). Thus the geographical patterns of water vapor and lapse rate feedbacks are not correlated, but when the feedbacks are calculated in precipitation percentiles rather than in geographical space they are anticorrelated, with strong positive water vapor feedback associated with strong negative lapse rate feedback. The regional structure of the feedbacks is not related to the strength of the tropical-mean feedback in a subset of the climate models from the CMIP5 archive. Nevertheless the approach constitutes a useful process-based test of climate models and has the potential to be extended to constrain regional climate projections.

Regional Dynamical Downscaling and the CORDEX Initiative

We review the challenges and future perspectives of regional climate model (RCM), or dynamical downscaling, activities. Among the main technical issues in need of better understanding are those of selection and sensitivity to the model domain and resolution, techniques for providing lateral boundary conditions, and RCM internal variability. The added value (AV) obtained with the use of RCMs remains a central issue, which needs more rigorous and comprehensive analysis strategies. Within the context of regional climate projections, large ensembles of simulations are needed to better understand the models and characterize uncertainties. This has provided an impetus for the development of the Coordinated Regional Downscaling Experiment (CORDEX), the first international program offering a common protocol for downscaling experiments, and we discuss how CORDEX can address the key scientific challenges in downscaling research. Among the main future developments in RCM research, we highlight the development of coupled regional Earth system models and the transition to very high-resolution, cloud-resolving models.

Assessing water resource system vulnerability to unprecedented hydrological drought using copulas to characterize drought duration and deficit.

Global climate models suggest an increase in evapotranspiration, changing storm tracks, and moisture delivery in many parts of the world, which are likely to cause more prolonged and severe drought, yet the weakness of climate models in modeling persistence of hydroclimatic variables and the uncertainties associated with regional climate projections mean that impact assessments based on climate model output may underestimate the risk of multiyear droughts. In this paper, we propose a vulnerability‐based approach to test water resource system response to drought. We generate a large number of synthetic streamflow series with different drought durations and deficits and use them as input to a water resource system model. Marginal distributions of the streamflow for each month are generated by bootstrapping the historical data, while the joint probability distributions of consecutive months are constructed using a copula‐based method. Droughts with longer durations and larger deficits than the observed record are generated by perturbing the copula parameter and by adopting an importance sampling strategy for low flows. In this way, potential climate‐induced changes in monthly hydrological persistence are factored into the vulnerability analysis. The method is applied to the London water system (England) to investigate under which drought conditions severe water use restrictions would need to be imposed. Results indicate that the water system is vulnerable to drought conditions outside the range of historical events. The vulnerability assessment results were coupled with climate model information to compare alternative water management options with respect to their vulnerability to increasingly long and severe drought.

Large-Scale Range Collapse of Hawaiian Forest Birds under Climate Change and the Need for 21st Century Conservation Options [corrected

Hawaiian forest birds serve as an ideal group to explore the extent of climate change impacts on at-risk species. Avian malaria constrains many remaining Hawaiian forest bird species to high elevations where temperatures are too cool for malaria’s life cycle and its principal mosquito vector. The impact of climate change on Hawaiian forest birds has been a recent focus of Hawaiian conservation biology, and has centered on the links between climate and avian malaria. To elucidate the differential impacts of projected climate shifts on species with known varying niches, disease resistance and tolerance, we use a comprehensive database of species sightings, regional climate projections and ensemble distribution models to project distribution shifts for all Hawaiian forest bird species. We illustrate that, under a likely scenario of continued disease-driven distribution limitation, all 10 species with highly reliable models (mostly narrow-ranged, single-island endemics) are expected to lose >50% of their range by 2100. Of those, three are expected to lose all range and three others are expected to lose >90% of their range. Projected range loss was smaller for several of the more widespread species; however improved data and models are necessary to refine future projections. Like other at-risk species, Hawaiian forest birds have specific habitat requirements that limit the possibility of range expansion for most species, as projected expansion is frequently in areas where forest habitat is presently not available (such as recent lava flows). Given the large projected range losses for all species, protecting high elevation forest alone is not an adequate long-term strategy for many species under climate change. We describe the types of additional conservation actions practitioners will likely need to consider, while providing results to help with such considerations.

Towards predictive understanding of regional climate change

This Review considers recent advances in our understanding of regional climate change, critically discusses outstanding issues, and recommends targets for future research. Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.

Irreducible uncertainty in near-term climate projections

Model simulations of the next few decades are widely used in assessments of climate change impacts and as guidance for adaptation. Their non-linear nature reveals a level of irreducible uncertainty which it is important to understand and quantify, especially for projections of near-term regional climate. Here we use large idealised initial condition ensembles of the FAMOUS global climate model with a 1 %/ year compound increase in CO2 levels to quantify the range of future temperatures in model-based projections. These simulations explore the role of both atmospheric and oceanic initial conditions and are the largest such ensembles to date. Short-term simulated trends in global temperature are diverse, and cooling periods are more likely to be followed by larger warming rates. The spatial pattern of near-term temperature change varies considerably, but the proportion of the surface showing a warming is more consistent. In addition, ensemble spread in inter-annual temperature declines as the climate warms, especially in the North Atlantic. Over Europe, atmospheric initial condition uncertainty can, for certain ocean initial conditions, lead to 20 year trends in winter and summer in which every location can exhibit either strong cooling or rapid warming. However, the details of the distribution are highly sensitive to the ocean initial condition chosen and particularly the state of the Atlantic meridional overturning circulation. On longer timescales, the warming signal becomes more clear and consistent amongst different initial condition ensembles. An ensemble using a range of different oceanic initial conditions produces a larger spread in temperature trends than ensembles using a single ocean initial condition for all lead times. This highlights the potential benefits from initialising climate predictions from ocean states informed by observations. These results suggest that climate projections need to be performed with many more ensemble members than at present, using a range of ocean initial conditions, if the uncertainty in nearterm regional climate is to be adequately quantified.

Past and future warming of a deep European lake (Lake Lugano): What are the climatic drivers?

We used four decades (1972–2013) of temperature data from Lake Lugano, Switzerland and Italy, to address the hypotheses that: [i] the lake has been warming; [ii] part of the warming reflects global trends and is independent from climatic oscillations and [iii] the lake will continue to warm until the end of the 21st century. During the time spanned by our data, the surface waters of the lake (0–5 m) warmed at rates of 0.2–0.9 °C per decade, depending on season. The temperature of the deep waters (50-m bottom) displayed a rising trend in a meromictic basin of the lake and a sawtooth pattern in the other basin, which is holomictic. Long-term variation in surface-water temperature correlated to global warming and multidecadal variation in two climatic oscillations, the Atlantic Multidecadal Oscillation (AMO) and the East Atlantic Pattern (EA). However, we did not detect an influence of the EA on the lake's temperature (as separate from the effect of global warming). Moreover, the effect of the AMO, estimated to a maximum of + 1 °C, was not sufficient to explain the observed temperature increase (+ 2–3 °C in summer). Based on regional climate projections, we predicted that the lake will continue to warm at least until the end of the 21st century. Our results strongly suggest that the warming of Lake Lugano is tied to global climate change. To sustain current ecosystem conditions in Lake Lugano, we suggest that management plans that curtail eutrophication and (or) mitigation of global warming be pursued.

Future trends of snowfall days in northern Spain from ENSEMBLES regional climate projections

In a previous study Pons et al. (Clim Res 54(3):197–207, 2010. doi: 10.3354/cr01117g ) reported a significant decreasing trend of snowfall occurrence in the Northern Iberian Peninsula since the mid 70s. The study was based on observations of annual snowfall frequency (measured as the annual number of snowfall days NSD) from a network of 33 stations ranging from 60 to 1350 m. In the present work we analyze the skill of Regional Climate Models (RCMs) to reproduce this trend for the period 1961–2000 (using both reanalysis- and historical GCM-driven boundary conditions) and the trend and the associated uncertainty of the regional future projections obtained under the A1B scenario for the first half of the twenty-first century. In particular, we consider the regional simulation dataset from the EU-funded ENSEMBLES project, consisting of thirteen state-of-the-art RCMs run at 25 km resolution over Europe. While ERA40 severely underestimates both the mean NSD and its observed trend (−2.2 days/decade), the corresponding RCM simulations driven by the reanalysis appropriately capture the interannual variability and trends of the observed NSD (trends ranging from −3.4 to −0.7, −2.1 days/decade for the ensemble mean). The results driven by the GCM historical runs are quite variable, with trends ranging from −8.5 to 0.2 days/decade (−1.5 days/decade for the ensemble mean), and the greatest uncertainty by far being associated with the particular GCM used. Finally, the trends for the future 2011–2050 A1B runs are more consistent and significant, ranging in this case from −3.7 to −0.5 days/decade (−2.0 days/decade for the ensemble mean), indicating a future significant decreasing trend. These trends are mainly determined by the increasing temperatures, as indicated by the interannual correlation between temperature and NSD (−0.63 in the observations), which is preserved in both ERA40- and GCM-driven simulations.

Applying the Triangle Method for the parameterization of irrigated areas as input for spatially distributed hydrological modeling — Assessing future drought risk in the Gaza Strip (Palestine)

In the Mediterranean region, particularly in the Gaza strip, an increased risk of drought is among the major concerns related to climate change. The impacts of climate change on water availability, drought risk and food security can be assessed by means of hydro-climatological modeling. However, the region is prone to severe observation data scarcity, which limits the potential for robust model parameterization, calibration and validation. In this study, the physically based, spatially distributed hydrological model WaSiM is parameterized and evaluated using satellite imagery to assess hydrological quantities. The Triangle Method estimates actual evapotranspiration (ETR) through the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) provided by Landsat TM imagery. So-derived spatially distributed evapotranspiration is then used in two ways: first a subset of the imagery is used to parameterize the irrigation module of WaSiM and second, withheld scenes are applied to evaluate the performance of the hydrological model in the data scarce study area. The results show acceptable overall correlation with the validation scenes (r=0.53) and an improvement over the usual irrigation parameterization scheme using land use information exclusively. This model setup is then applied for future drought risk assessment in the Gaza Strip using a small ensemble of four regional climate projections for the period 2041–2070. Hydrological modeling reveals an increased risk of drought, assessed with an evapotranspiration index, compared to the reference period 1971–2000. Current irrigation procedures cannot maintain the agricultural productivity under future conditions without adaptation.

The VIADUC project: innovation in climate adaptation through service design

Abstract. From the French National Adaptation to Climate Change Plan, the "Drias, les futurs du climat" service has been developed to provide easy access to French regional climate projections. This is a major step for the implementation of French Climate Services. The usefulness of this service for the end-users and decision makers involved with adaptation planning at a local scale is investigated. As such, the VIADUC project is: to evaluate and enhance Drias, as well as to imagine future development in support of adaptation. Climate scientists work together with end-users and a service designer. The designer's role is to propose an innovative approach based on the interaction between scientists and citizens. The chosen end-users are three Natural Regional Parks located in the South West of France. The latter parks are administrative entities which gather municipalities having a common natural and cultural heritage. They are also rural areas in which specific economic activities take place, and therefore are concerned and involved in both protecting their environment and setting-up sustainable economic development. The first year of the project has been dedicated to investigation including the questioning of relevant representatives. Three key local economic sectors have been selected: i.e. forestry, pastoral farming and building activities. Working groups were composed of technicians, administrative and maintenance staff, policy makers and climate researchers. The sectors' needs for climate information have been assessed. The lessons learned led to actions which are presented hereinafter.

Climate-water quality relationships in Texas reservoirs

Water temperature, dissolved oxygen, and concentrations of salts in surface water bodies can be affected by the natural environment and local human activities such as surface and ground water withdrawals, land use and energy extraction, and variability and long-term trends in atmospheric conditions including temperature and precipitation. Here, we quantify the relationship between 121 indicators of mean and extreme temperature and precipitation and 24 water quality parameters in 57 Texas reservoirs using observational data records covering the period 1960 to 2010. Over time scales ranging from 1 week to 2 years, we find that water temperature, dissolved oxygen, pH, specific conductance, chloride, sulfate, and phosphorus all show consistent correlations with atmospheric predictors, including high and low temperature extremes, dry days, heavy precipitation events, and mean temperature and precipitation. Based on these relationships combined with regional climate projections, we expect climate change to increase water temperatures, decrease dissolved oxygen levels, decrease pH, increase specific conductance, and increase levels of sulfate and chloride in Texas reservoirs. Over decadal time scales, this may affect aquatic ecosystems in the reservoirs, including altering the risk of conditions conducive to algae occurrence, as well as affecting the quality of water available for human consumption and recreation. Copyright © 2015 John Wiley & Sons, Ltd.

Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios

Hindcast and 21st century winds, simulated by General Circulation Models (GCMs), were used to drive global- and regional-scale spectral wind-wave generation models in the Pacific Ocean Basin to assess future wave conditions along the margins of the North American west coast and Hawaiian Islands. Three-hourly winds simulated by four separate GCMs were used to generate an ensemble of wave conditions for a recent historical time-period (1976–2005) and projections for the mid and latter parts of the 21st century under two radiative forcing scenarios (RCP 4.5 and RCP 8.5), as defined by the fifth phase of the Coupled Model Inter-comparison Project (CMIP5) experiments. Comparisons of results from historical simulations with wave buoy and ERA-Interim wave reanalysis data indicate acceptable model performance of wave heights, periods, and directions, giving credence to generating projections. Mean and extreme wave heights are projected to decrease along much of the North American west coast. Extreme wave heights are projected to decrease south of ∼50°N and increase to the north, whereas extreme wave periods are projected to mostly increase. Incident wave directions associated with extreme wave heights are projected to rotate clockwise at the eastern end of the Aleutian Islands and counterclockwise offshore of Southern California. Local spatial patterns of the changing wave climate are similar under the RCP 4.5 and RCP 8.5 scenarios, but stronger magnitudes of change are projected under RCP 8.5. Findings of this study are similar to previous work using CMIP3 GCMs that indicates decreasing mean and extreme wave conditions in the Eastern North Pacific, but differ from other studies with respect to magnitude and local patterns of change. This study contributes toward a larger ensemble of global and regional climate projections needed to better assess uncertainty of potential future wave climate change, and provides model boundary conditions for assessing the impacts of climate change on coastal systems.

Statistical multi-model climate projections of surface ocean waves in Europe

In recent years, the impact of climate change on sea surface waves has received increasingly more attention by the climate community. Indeed, ocean waves reaching the coast play an important role in several processes concerning coastal communities, such as inundation and erosion. However, regional downscaling at the high spatial resolution necessary for coastal studies has received less attention. Here, we present a novel framework for regional wave climate projections and its application in the European region. Changes in the wave dynamics under different scenarios in the Northeast Atlantic Ocean and the Mediterranean are analyzed.The multi-model projection methodology is based on a statistical downscaling approach. The statistical relation between the predictor (atmospheric conditions) and the predictand (multivariate wave climate) is based on a weather type (WT) classification. This atmospheric classification is developed by applying the k-means clustering technique over historical offshore sea level pressure (SLP) fields. Each WT is linked to sea wave conditions from a wave hindcast. This link is developed by associating atmospheric conditions from reanalysis with multivariate local waves. This predictor–predictand relationship is applied to the daily SLP fields from global climate models (GCMs) in order to project future changes in regional wave conditions. The GCMs used in the multi-model projection are selected according to skill criteria. The application of this framework uses CMIP5-based wave climate projections in Europe. The low computational requirements of the statistical approach allow a large number of GCMs and climate change scenarios to be studied.Consistent with previous works on global wave climate projections, the estimated changes from the regional wave climate projections show a general decrease in wave heights and periods in the Atlantic Europe for the late twenty-first century. The regional projections, however, allow a more detailed spatial characterization of the projected changes under different climate scenarios. For example, changes in significant wave heights for the RCP8.5 scenario for the 2070–2099 time period indicate a general decrease of about 10 cm in Southern Europe (Portuguese, Spanish and French coasts) with respect to present conditions. This decrease is due to a higher occurrence of dominant and moderate Azores high pressure systems over the North Atlantic Ocean and a decrease in the persistence of intense low pressure systems at high latitudes.

Regional climate projection over South Korea simulated by the HadGEM2-AO and WRF model chain under RCP emission scenarios

This study assesses the regional climate projection newly generated within the framework of the national downscaling project in South Korea. To obtain fine-scale climate information (12.5 km), dynamical downscaling of the HadGEM2-AO global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios is performed using the Weather Research and Forecasting (WRF) modeling system. Changes in temperature and precipitation in terms of long-term trends, daily characteristics and extremes are presented by comparing two 30 yr periods (2041−2070 vs. 2071−2100) in which increasing rates of emission forcing between the RCP4.5 and RCP8.5 scenarios are relatively similar and quite different, respectively. The temperature increase presents a relevant trend, but the degree of warming varies in different periods and emission scenarios. While the temperature distribution from the RCP8.5 projection is continuously shifted toward warmer conditions by the end of the 21st century, the RCP4.5 projection appears to stabilize warming in accordance with emission forcing. This shift in distribution directly affects the magnitude of extremes, which enhances extreme hot days but reduces extreme cold days. Precipitation changes, however, do not respond monotonically to emission forcing, as they exhibit less sensitivity to different emission scenarios. An enhancement of high intensity precipitation and a reduction of weak intensity precipitation are discernible, implying an intensified hydrologic cycle. Changes in return levels of annual maximum precipitation suggest an increased probability of extreme precipitation with 20 yr and 50 yr return periods.

Robustness of Ensemble Climate Projections Analyzed with Climate Signal Maps: Seasonal and Extreme Precipitation for Germany

Climate signal maps can be used to identify regions where robust climate changes can be derived from an ensemble of climate change simulations. Here, robustness is defined as a combination of model agreement and the significance of the individual model projections. Climate signal maps do not show all information available from the model ensemble, but give a condensed view in order to be useful for non-climate scientists who have to assess climate change impact during the course of their work. Three different ensembles of regional climate projections have been analyzed regarding changes of seasonal mean and extreme precipitation (defined as the number of days exceeding the 95th percentile threshold of daily precipitation) for Germany, using climate signal maps. Although the models used and the scenario assumptions differ for the three ensembles (representative concentration pathway (RCP) 4.5 vs. RCP8.5 vs. A1B), some similarities in the projections of future seasonal and extreme precipitation can be seen. For the winter season, both mean and extreme precipitation are projected to increase. The strength, robustness and regional pattern of this increase, however, depends on the ensemble. For summer, a robust decrease of mean precipitation can be detected only for small regions in southwestern Germany and only from two of the three ensembles, whereas none of them projects a robust increase of summer extreme precipitation.

Technical challenges and solutions in representing lakes when using WRF in downscaling applications

Abstract. The Weather Research and Forecasting (WRF) model is commonly used to make high-resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional downscaled fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for downscaling simulations are presented and contrasted.

The optimal choice of hydrodynamic models to assess the impact of climate change on the cryosphere

This study is targeted at narrowing the range of uncertainties in predictive cryospheric modeling associated with climatic projections. We used the output from 36 CMIP5 GCM runs for the period 1976–2005 and calculated trends of several climatic characteristics that largely govern the state of the cryosphere, i.e. seasonal and mean annual air temperature, thawing degree-day sums, annual and winter precipitation sums. Data from 744 weather stations were used to identify and delineate 17 regions, which demonstrate coherent temperature changes in the past decades. Results from GCMs and observations were averaged over the «coherent regions» and compared with each other. Ultimately, we evaluated the skills of individual CMIP5 GCMs, ranked them in the specific context of predictive cryospheric modeling, identified top-end models in each of the 17 regions and eliminated the outliers. Selected top-end GCMs were used to compose optimal regional ensembles that were compared with the ensemble consisting of all available models. An optimal ensemble was also constructed for the area underlain by permafrost in Russia. Results indicate that the all-model ensemble in most regions underestimates the projected temperature changes compared to the optimal ensemble. Elimination of the outliers narrows the range of uncertainty in regional climate projection by 5–20%.