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Present study attempts to project extreme precipitation indices over 34 different meteorological subdivisions and six homogeneous regions such as Northwest, Central Northeast, Northeast, West Central, Peninsular India and Hilly Region during summer monsoon season in the twenty-first century. For this purpose, the Regional Climate Model version4 (RegCM4) had been run at 50 km horizontal resolution forced with the global model GFDL-ESM2 M, during reference period 1976–2005 for the model validation, and the mid- (2031–2060) and far-future (2070–2099) for projections under RCP8.5 scenario over the South Asia CORDEX domain. In this paper, model simulated precipitation has been validated against IMD, APHRODITE and NCEP/NCAR data sets. The results indicate that RegCM4 captures the important features of seasonal precipitation and various extreme indices over the study area. The RegCM4 has projected an increase in the mean seasonal precipitation by 0.56 mm/day whereas in case of GFDL model the rate is 0.39 mm/day during the far-future relative to the reference period. The heavy precipitation indices are projected to increase more frequently (0.264/decade) than the mean precipitation rate (0.01/decade) over India. The correlations between the extreme precipitation indices and the seasonal mean precipitation are found to be strong. In addition, the consecutive dry days are projected to occur more frequently (3–5 days) over West Central (Telangana, Vidarbha and Marathwada) and west Rajasthan while consecutive wet days are projected to decrease over larger parts of India during far-future. Similarly, 1 day maximum precipitation and the simple daily intensity index are projected to increase consistently from mid- to far- futures over some sub-divisions of West coast, Hilly and Northeast regions. From a spatial probability perspective, model projection indicates more frequent severe drought and flood conditions over India. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to elucidate a potential use of the large samples of seasonal means that hindcasts provide for investigating different aspects of climate variability. This use of hindcasts complements their traditional uses in bias correction, real-time forecast calibration, and prediction skill assessment. For seasonal hindcast data from NCEP CFSv2 we show that a sample size 5208 for each target season is achievable. To demonstrate the utility of the proposed concept, we use this large sample dataset to illustrate how it could be used in documenting spatial variability in various moments of seasonal mean precipitation PDF over the US, and further, quantify nuances in the variations in precipitation PDF at different geographical locations with the amplitude of ENSO SSTs. It is our hope that analysis presented in this paper will accelerate utilization of seasonal hindcast datasets in furthering our understanding of different aspects of climate variability. With the advantage of the large sample size, we demonstrated that the precipitation PDF at the each grid of the CONUS can be represented by gamma distribution for a more concise and effective way to summarize precipitation variability. The availability of the large sample dataset also allowed us to analyze the statistical characteristic of the precipitation responses to the different amplitudes of ENSO SSTs. The results show that for strong warm events, enhancement in precipitation has larger amplitude than decrease in precipitation for cold events in the regions of Southern California and southeastern US. The variation of the precipitation signal over the other sub-regions including the southwestern US, mid-northwest, and mid-east shows more linear relationship with the ENSO SSTs. In response to anomalous ENSO SSTs, although the PDF of December-January-February seasonal mean precipitation anomaly is shifted from its climatological PDF, there is still a large overlap between precipitation PDFs for ENSO and its climatological counterpart. This uncertainty in seasonal mean outcomes of precipitation, therefore, limits the seasonal prediction skill. [ABSTRACT FROM AUTHOR]

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

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

The objective of this paper is to understand how large-scale processes, cloud cover and surface fluxes affect the temperature variability over the SIRTA site, near Paris, and in a regional climate simulation performed in the frame of HyMeX/Med-CORDEX programs. This site is located in a climatic transitional area where models usually show strong dispersions despite the significant influence of large scale on interannual variability due to its western location. At seasonal time scale, the temperature is mainly controlled by surface fluxes. In the model, the transition from radiation to soil moisture limited regime occurs earlier than in observations leading to an overestimate of summertime temperature. An overestimate of shortwave radiation (SW), consistent with a lack of low clouds, enhances the soil dryness. A simulation with a wet soil is used to better analyse the relationship between dry soil and clouds but while the wetter soil leads to colder temperature, the cloud cover during daytime is not increased due to the atmospheric stability. At shorter time scales, the control of surface radiation becomes higher. In the simulation, higher temperatures are associated with higher SW. A wet soil mitigates the effect of radiation due to modulation by evaporation. In observations, the variability of clouds and their effect on SW is stronger leading to a nearly constant mean SW when sorted by temperature quantile but a stronger impact of cloud cover on day-to-day temperature variability. Impact of cloud albedo effect on precipitation is also compared. [ABSTRACT FROM AUTHOR]

Global Climate Models (GCMs) have been extensively used in many climate change impact studies. However, the coarser resolution of these GCM outputs is not adequate to assess the potential effects of climate change on local scale. Downscaling techniques have thus been proposed to resolve this problem either by dynamical or statistical approaches. The statistical downscaling (SD) methods are widely privileged because of their simplicity of implementation and use. However, many of them ignore the observed spatial dependence between different locations, which significantly affects the impact study results. An improved multi-site SD approach is thus presented in this paper to downscaling of daily precipitation at many sites concurrently. This approach is based on a combination of multiple regression models for rainfall occurrences and amounts and the Singular Value Decomposition technique, which models the stochastic components of these regression models to preserve accurately the space-time statistical properties of the daily precipitation. Furthermore, this method was able to describe adequately the intermittency property of the precipitation processes. The proposed approach has been assessed using 10 rain gauges located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the National Centers for Environmental Prediction/National Centre for Atmospheric Research re-analysis data set. The results have indicated the ability of the proposed approach to reproduce accurately multiple observed statistical properties of the precipitation occurrences and amounts, the at-site temporal persistence, the spatial dependence between sites and the temporal variability and spatial intermittency of the precipitation processes. [ABSTRACT FROM AUTHOR]

How soil moisture affects precipitation is an important question-with far reaching consequences, from weather prediction to centennial climate change-, albeit a poorly understood one. In this paper, an analysis of soil moisture-precipitation interactions over France based on observations is presented. A first objective of this paper is to investigate how large scale circulation modulates soil moisture-precipitation interactions, thanks to a weather regime approach. A second objective is to study the influence of soil moisture not only on precipitation but also on the difference between precipitation and evapotranspiration. Indeed, to have a total positive soil moisture-precipitation feedback, the potential decrease in precipitation associated with drier soils should be larger than the decrease in evapotranspiration that drier soils may also cause. A potential limited impact of soil moisture on precipitation is found for some weather regimes, but its sign depends on large scale circulation. Indeed, antecedent dry soil conditions tend to lead to smaller precipitation for the negative phase of the North Atlantic Oscillation (NAO) regime but to larger precipitation for the Atlantic Low regime. This differential response of precipitation to soil moisture anomalies depending on large scale circulation is traced back to different responses of atmospheric stability. For all circulation regimes, dry soils tend to increase the lifted condensation level, which is unfavorable to precipitation. But for the negative phase of the NAO, low soil moisture tends to lead to an increase of atmospheric stability while it tends to lead to a decrease of stability for Atlantic Low. Even if the impact of soil moisture anomalies varies depending on large scale circulation (it is larger for Atlantic low and the positive phase of the NAO), dry soils always lead to a decrease in evapotranspiration. As the absolute effect of antecedent soil moisture on evapotranspiration is always much larger than its effects on precipitation, for all circulation regimes dry soil anomalies subsequently lead to positive precipitation minus evapotranspiration anomalies i.e. the total soil moisture feedback is found to be negative. This negative feedback is stronger for the Atlantic Low and the positive phase of the NAO regimes. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

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

Orographic precipitation and snowpack provide a vital water resource for the western U.S., while convective precipitation accounts for a significant part of annual precipitation in the eastern U.S. As a result, water managers are keenly interested in their fate under climate change. However, previous studies of water cycle changes in the U.S. have been conducted with climate models of relatively coarse resolution, leading to potential misrepresentation of key physical processes. This paper presents results from a high-resolution climate change simulation that permits convection and resolves mesoscale orography at 4-km grid spacing over much of North America using the Weather Research and Forecasting (WRF) model. Two 13-year simulations were performed, consisting of a retrospective simulation (October 2000-September 2013) with initial and boundary conditions from ERA-interim and a future climate sensitivity simulation with modified reanalysis-derived initial and boundary conditions through adding the CMIP5 ensemble-mean high-end emission scenario climate change. The retrospective simulation is evaluated by validating against Snowpack Telemetry (SNOTEL) and an ensemble of gridded observational datasets. It shows overall good performance capturing the annual/seasonal/sub-seasonal precipitation and surface temperature climatology except for a summer dry and warm bias in the central U.S. In particular, the WRF seasonal precipitation agrees with SNOTEL observations within a few percent over the mountain ranges, providing confidence in the model's estimation of western U.S. seasonal snowfall and snowpack. The future climate simulation forced with warmer and moister perturbed boundary conditions enhances annual and winter-spring-fall seasonal precipitation over most of the contiguous United States (CONUS), but suppresses summertime precipitation in the central U.S. The WRF-downscaled climate change simulations provide a high-resolution dataset (i.e., High-Resolution CONUS downscaling, HRCONUS) to the community for studying one possible scenario of regional climate changes and impacts. [ABSTRACT FROM AUTHOR]

The Arctic cell as a reversed and closed loop next to the Polar cell has been recently revealed in the Northern Hemisphere (NH). In this paper, we study the interannual variability of the Arctic and Polar cell strengths during 1979-2012, and their influence on surface air temperature (SAT), precipitation, and sea-ice concentration (SIC) at mid- and high-latitudes of the NH. We show that there is a significant negative correlation between the Arctic and Polar cell strengths. Both the Arctic and Polar cell strengths can well indicate the recurring climatic anomalies of SAT, precipitation, and SIC in their extreme winters. The surface large-scale cold-warm and dry-wet anomalous patterns in these extreme winters are directly linked with the vertical structure of height and temperature anomalies in the troposphere. Results suggest that the past climatic anomalies are better indicated by the strength anomalies of the Polar and Arctic cells than the traditional indices of mid-high latitude pattern such as the Arctic Oscillation and North Atlantic Oscillation. This study illustrates a three-dimensional picture of atmospheric variable anomalies in the troposphere that result in surface climatic anomalies. [ABSTRACT FROM AUTHOR]

The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project's strategy is to apply prescribed observationally based anomaly forcing, i.e., 'idealized but realistic' forcing, in simulations by climate models. The goal is to assess these forcings' effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the twentieth century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60 % of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40 % of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought. [ABSTRACT FROM AUTHOR]

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)-two contrasting SD methods-in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector. [ABSTRACT FROM AUTHOR]

The Andes Cordillera plays a role in driving moisture and heat from tropical onto subtropical South America. It forces the development of a lee-side trough that covers most of western Argentina and a low-level jet that maximizes over Paraguay, eastern Bolivia and northern Argentina and is tightly linked to precipitation variability over much of central and southeastern South America. Its steep slopes and the large zonal gradients in topography between the Equator and 40°S are misrepresented in climate simulations using Global Climate Models (GCM) with resolutions coarser than about 100 km, since they naturally have a poor representation of the Andes and related circulation features. This paper analyses the impact of varying artificially the altitude of the Andes Cordillera in a GCM as well as increasing the horizontal resolution to study how these variations determine moisture fluxes and precipitation over selected regions of South America. Results show that the height of the Andes is crucial in shaping moisture fluxes pathways onto subtropical South America all year long. In particular, the low-level jet is only simulated when the Andes heights are doubled. At the same time, the relationship between the Andes shape and the location of the Bolivian High in summer is also discussed. In terms of precipitation, the lowest bias in the simulations is achieved when the horizontal resolution is increased, while in particular near the Andes foothills the simulated annual rainfall is largely determined by the Mountains shape. [ABSTRACT FROM AUTHOR]

The unprecedented availability of 6-hourly data from a multi-model GCM ensemble in the CMIP5 data archive presents the new opportunity to dynamically downscale multiple GCMs to develop high-resolution climate projections relevant to detailed assessment of climate vulnerability and climate change impacts. This enables the development of high resolution projections derived from the same set of models that are used to characterise the range of future climate changes at the global and large-scale, and as assessed in the IPCC AR5. However, the technical and human resource required to dynamically-downscale the full CMIP5 ensemble are significant and not necessary if the aim is to develop scenarios covering a representative range of future climate conditions relevant to a climate change risk assessment. This paper illustrates a methodology for selecting from the available CMIP5 models in order to identify a set of 8-10 GCMs for use in regional climate change assessments. The selection focuses on their suitability across multiple regions-Southeast Asia, Europe and Africa. The selection (a) avoids the inclusion of the least realistic models for each region and (b) simultaneously captures the maximum possible range of changes in surface temperature and precipitation for three continental-scale regions. We find that, of the CMIP5 GCMs with 6-hourly fields available, three simulate the key regional aspects of climate sufficiently poorly that we consider the projections from those models 'implausible' ( MIROC- ESM, MIROC- ESM- CHEM, and IPSL- CM5B- LR). From the remaining models, we demonstrate a selection methodology which avoids the poorest models by including them in the set only if their exclusion would significantly reduce the range of projections sampled. The result of this process is a set of models suitable for using to generate downscaled climate change information for a consistent multi-regional assessment of climate change impacts and adaptation. [ABSTRACT FROM AUTHOR]

Future changes in extreme multi-day precipitation will influence the probability of floods in the river Rhine basin. In this paper the spread of the changes projected by climate models at the end of this century (2081-2100) is studied for a 17-member ensemble of a single Global Climate Model (GCM) and results from the Coupled Model Intercomparison Project Phase 3 (CMIP3) ensemble. All climate models were driven by the IPCC SRES A1B emission scenario. An analysis of variance model is formulated to disentangle the contributions from systematic differences between GCMs and internal climate variability. Both the changes in the mean and characteristics of extremes are considered. To estimate variances due to internal climate variability a bootstrap method was used. The changes from the GCM simulations were linked to the local scale using an advanced non-linear delta change approach. This approach uses climate responses of the GCM to transform the daily precipitation of 134 sub-basins of the river Rhine. The transformed precipitation series was used as input for the hydrological Hydrologiska Byråns Vattenbalansavdelning model to simulate future river discharges. Internal climate variability accounts for about 30 % of the total variance in the projected climate trends of average winter precipitation in the CMIP3 ensemble and explains a larger fraction of the total variance in the projected climate trends of extreme precipitation in the winter half-year. There is a good correspondence between the direction and spread of the changes in the return levels of extreme river discharges and extreme 10-day precipitation over the Rhine basin. This suggests that also for extreme discharges a large fraction of the total variance can be attributed to internal climate variability. [ABSTRACT FROM AUTHOR]

In this paper the forecast skill provided by the tropical Pacific Sea Surface Temperature (SST) associated with El Niño and El Niño Modoki over seasonal precipitation in Colombia has been evaluated through a lagged singular value decomposition analysis. The seasonal rainfall in many parts of the country can be predicted with suitable skill using the SST with a lead of one to four seasons. The response of precipitation to different phases of El Niño or El Niño Modoki is consistent with the state of the atmospheric fields of vertical velocity and velocity potential. Winter presents the best results for the prediction model, explaining the greatest percentage of square covariance fraction between the SST and lagged precipitation anomalies. The predictive capacity of these variability modes of SST for rainfall in Colombia is comparable or even higher in some seasons, particularly in winter, than the SST ability to explain the behavior of the precipitation during coetaneous seasons. The forecast of seasonal precipitation in Colombia validated in this work could be useful for planning and managing natural water resources in some regions of the country, particularly for the driest season of the year. [ABSTRACT FROM AUTHOR]

A regional climate modelling system, the Providing REgional Climates for Impacts Studies developed by the Hadley Centre for Climate Prediction and Research, has been used to study future climate change scenarios over Indus basin for the impact assessment. In this paper we have examined the three Quantifying Uncertainty in Model Predictions simulations selected from 17-member perturbed physics ensemble generated using Hadley Centre Coupled Module. The climate projections based on IPCC SRES A1B scenario are analysed over three time slices, near future (2011-2040), middle of the twenty first century (2041-2070), and distant future (2071-2098). The baseline simulation (1961-1990) was evaluated with observed data for seasonal and spatial patterns and biases. The model was able to resolve features on finer spatial scales and depict seasonal variations reasonably well, although there were quantitative biases. The model simulations suggest a non-uniform change in precipitation overall, with an increase in precipitation over the upper Indus basin and decrease over the lower Indus basin, and little change in the border area between the upper and lower Indus basins. A decrease in winter precipitation is projected, particularly over the southern part of the basin. Projections indicate greater warming in the upper than the lower Indus, and greater warming in winter than in the other seasons. The simulations suggest an overall increase in the number of rainy days over the basin, but a decrease in the number of rainy days accompanied by an increase in rainfall intensity in the border area between the upper and lower basins, where the rainfall amount is highest. [ABSTRACT FROM AUTHOR]

A large intraseasonal variation in geopotential height over the Central Asia region, where the Asian subtropical jet is located, occurs between May and June, and the most dominant variation has a wave-like distribution. This variation in geopotential height influences precipitation across South and Southeast Asia. In this paper, we use composite analysis to determine the causes of this intraseasonal variation over Central Asia. The wave train propagates from the northern Atlantic Ocean to Central Asia over a period of a week, and generates an anomaly in geopotential height over the region. The tropical disturbance, which is similar to the Madden-Julian oscillation, appears a few days before the maximum of the anticyclonic anomaly over Central Asia, and is accompanied by active convection over the Indian Ocean and suppressed convection over Central America. Results of numerical experiments using a linear baroclinic model show that the active convection over the northern Indian Ocean causes the anticyclonic anomaly over Central Asia. The wave train that extends from the northern Atlantic Ocean to Central Asia is generated by negative thermal forcing over Central America, and the phase distribution of this wave train is similar to that observed in the composite analysis. Central Asia is the region where the effects of the tropics and middle latitudes overlap, and it is an important connection point between the Asian monsoon and middle latitudes. [ABSTRACT FROM AUTHOR]

Based on numerical experiments using the NCAR CAM3-CLM3 models, this paper examines the impact of soil moisture, vegetation, and sea surface temperature (SST) on the inter-annual variability of climate over land. For each element, two experiments are carried out, with the inter-annual variability preserved in one experiment and eliminated in the other. Differences in the standard deviation of the precipitation and air temperature at the inter-annual time scale are used to quantify the impacts from soil moisture dynamics, vegetation dynamics, and oceanic forcing. The impact of oceanic forcing is mainly limited to the Tropics, with the strongest signal in the equatorial zone, and moisture convergence is the key linkage between SST forcing and tropical precipitation. Soil moisture plays a significant role in climate variability during the rainy seasons of all semi-arid regions (which is consistent with many previous studies), and during the dry seasons of the humid Amazon. Evapotranspiration is identified as the main mechanism linking precipitation variability to soil moisture. Amazon is the only region where vegetation dynamics has a significant influence on precipitation variability. However, the impact of vegetation dynamics on temperature is strong over the US Great Plains in all four seasons and in the Amazon region during the dry and dry-to-wet transition seasons. [ABSTRACT FROM AUTHOR]

On a hemispheric scale, it is now well established that stratospheric ozone depletion has been the principal driver of externally forced atmospheric circulation changes south of the Equator in the last decades of the 20th Century. The impact of ozone depletion has been felt over the entire hemisphere, as reflected in the poleward drift of the midlatitude jet, the southward expansion of the summertime Hadley cell and accompanying precipitation trends deep into the subtropics. On a regional scale, however, surface impacts directly attributable to ozone depletion have yet to be identified. In this paper we focus on South Eastern South America (SESA), a region that has exhibited one of the largest wetting trends during the 20th Century. We study the impact of ozone depletion on SESA precipitation using output from 6 different climate models, spanning a wide range of complexity. In all cases we contrast pairs of model integrations with and without ozone depletion, but with all other forcings identically specified. This allows for unambiguous attribution of the computed precipitation trends. All 6 climate models consistently reveal that stratospheric ozone depletion results in a significant wetting of SESA over the period 1960-1999. Taken as a whole, these model results strongly suggest that the impact of ozone depletion on SESA precipitation has been as large as, and quite possibly larger than, the one caused by increasing greenhouse gases over the same period. [ABSTRACT FROM AUTHOR]

A fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships is proposed. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. The goals of this approach are to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation. At the first layer, gridded climate data are used to identify 'areas', i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. This paper describes the climate network inference and related network metrics, and compares network properties for different sea surface temperature reanalyses and precipitation data sets, and for a small sample of CMIP5 outputs. [ABSTRACT FROM AUTHOR]

The possible change in the characteristics of weather in the future should be considered as important as the mean climate change because the increasing risk of extremes is related to the variability on daily time scales. The weather characteristics can be represented by the climatological mean interdiurnal (day-to-day) variability (MIDV). This paper first assessed the phase five of the Coupled Model Intercomparison Project coupled climate models’ capability to represent MIDV for the surface maximum and minimum temperature, surface wind speed and precipitation under the present climate condition. Based on the assessment, we selected three best models for projecting future change. We found that the future changes in MIDV are characterized by: (a) a marked reduction in surface maximum and minimum temperature over high latitudes during the cold season; (b) a stronger reduction in the surface minimum temperature than in the maximum temperature; (c) a reduction in surface wind speed over large parts of lands in Northern Hemisphere (NH) during NH spring; (d) a noticeable increase in precipitation in NH mid-high latitudes in NH spring and winter, and in particular over East Asia throughout most of the year. [ABSTRACT FROM AUTHOR]

This paper assesses the impacts of climate change on water resources in the upper Ping River Basin of Thailand. A rainfall-runoff model is used to estimate future runoff based on the bias corrected and downscaled ECHAM4/OPYC general circulation model (GCM) precipitation scenarios for three future 5-year periods; the 2023-2027 (2025s), the 2048-2052 (2050s) and 2093-2097 (2095s). Bias-correction and spatial disaggregation techniques are applied to improve the characteristics of raw ECHAM4/OPYC precipitation. Results of future simulations suggest a decrease of 13-19 % in annual streamflow compared to the base period (1998-2002). Results also indicate that there will be a shift in seasonal streamflow pattern. Peak flows in future periods will occur in October-November rather than September as observed in the base period. There will be a significant increase in the streamflow in April with overall decrease in streamflow during the rainy season (May-October) and an increase during the dry season (November-April) for all future time periods considered in the study. [ABSTRACT FROM AUTHOR]

This is the second of the two-part paper series on the analysis and evaluation of the Fifth phase of Coupled Model Intercomparison Project (CMIP5) simulation of contemporary climate as well as IPCC, AR5 Representative Concentrations Pathways (RCP), 4.5 and 8.5 scenarios projections of the Greater Horn of Africa (GHA) Climate. In the first part (Otieno and Anyah in Clim Dyn, ) we focused on the historical simulations, whereas this second part primarily focuses on future projections based on the two scenarios. Six Earth System Models (ESMs) from CMIP5 archive have been used to characterize projected changes in seasonal and annual mean precipitation, temperature and the hydrological cycle by the middle of twenty-first century over the GHA region, based on IPCC, 5th Assessment Report (AR5) RCP4.5 and RCP8.5 scenarios. Nearly all the models outputs analyzed reproduce the correct mean annual cycle of precipitation, with some biases among the models in capturing the correct peak of precipitation cycle, more so, March-April-May (MAM) seasonal rainfall over the equatorial GHA region. However, there is significant variation among models in projected precipitation anomalies, with some models projecting an average increase as others project a decrease in precipitation during different seasons. The ensemble mean of the ESMs indicates that the GHA region has been experiencing a steady increase in both precipitation and temperature beginning in the early 1980s and 1970s respectively in both RCP4.5 and RCP8.5 scenarios. Going by the ensemble means, temperatures are projected to steadily increase uniformly in all the seasons at a rate of 0.3/0.5 °C/decade under RCP4.5/8.5 scenarios over northern GHA region leading to an approximate temperature increase of 2/3 °C by the middle of the century. On the other hand, temperatures will likely increase at a rate of 0.3/0.4 °C/decade under RCP4.5/8.5 scenarios in both equatorial and southern GHA region leading to an approximate temperature increase of 2/2.5 °C by the middle of twenty-first century. Nonetheless, projected precipitation increase varied across seasons and sub-regions. With the exception of the equatorial region, that is projected to experience precipitation increase during DJF season, most sub-regions are projected to experience precipitation increase within their peak seasons, with the highest rate of increase experienced during DJF and OND seasons over southern and equatorial GHA regions respectively. Notably, as precipitation increases, the deficit (E < P) between evaporation (E) and precipitation (P) increased over the years, with a negatively skewed distribution. This generally implies that there is a high likelihood of an increased deficit in local moisture supply. This remarkable change in the general hydrological cycle (i.e. deficit in local moisture) is projected to be also coincident with intensified westerly anomaly influx from the Congo basin into the region. However, better understanding of the detailed changes in hydrological cycle will require comprehensive water budget analyses that require daily or sub-daily variables, and was not a specific focus of the present study. [ABSTRACT FROM AUTHOR]

This paper identifies two sources of uncertainties in model projections of temperature and precipitation: internal and inter-model variability. Eight models of WCRP-CMIP3 and WCRP-CMIP5 were compared to identify improvements in the reliability of projections from new generation models. While no significant differences are observed between both datasets, some improvements were found in the new generation models. For example, in summer CMIP5 inter-model variability of temperature was lower over northeastern Argentina, Paraguay and northern Brazil, in the last decades of the 21st century. Reliability of temperature projections from both sets of models is high, with signal to noise ratio greater than 1 over most of the study region. Although no major differences were observed in both precipitation datasets, CMIP5 inter-model variability was lower over northern and eastern Brazil in summer (especially at the end of the 21st century). Reliability of precipitation projections was low in both datasets. However, the signal to noise ratio in new generation models was close to 1, and even greater than 1 over eastern Argentina, Uruguay and southern Brazil in some seasons. [ABSTRACT FROM AUTHOR]

Precipitation over the contiguous United States exhibits large multi-decadal oscillations since the early twentieth century, and they often lead to dry (e.g., 1946-1976 and 1999-present) and wet (e.g., 1977-1998) periods and apparent precipitation trends (e.g., from the 1950s to 1990s) over most of the western and central US. The exact cause of these inter-decadal variations is not fully understood. Using observational and reanalysis data and model simulations, this paper examines the influence of the Inter-decadal Pacific Oscillation (IPO) on US precipitation. The IPO is a leading mode of sea surface temperatures (SSTs) seen mostly in the Pacific Ocean. It is found that decadal precipitation variations over much of the West and Central US, especially the Southwest, closely follow the evolution of the IPO ( r = 0.85 during 1923-2010 for the Southwest US), and the dry and wet periods are associated, respectively, with the cold and warm phases of the IPO. In particular, the apparent upward trend from the 1950s-1990s and the dry decade thereafter in precipitation over much of the West and Central US are largely caused by the IPO cycles, which switched to a warm phase around 1977 and back to a cold phase around 1999. An atmospheric model forced with observed SSTs reproduces much of this association of US precipitation with the IPO ( r = 0.95 between smoothed observed and simulated Southwest US precipitation during 1950-2009 and r = 0.88 between the simulated Southwest US precipitation and the IPO). Atmospheric reanalysis and model data both show a strong high (low) pressure center and anti-cyclonic (cyclonic) anomaly circulation over the North Pacific in the lower troposphere during cold (warm) phases of the IPO, which lead to dry and cold northwesterly and northerly winds and below-normal precipitation over much of the West US during IPO cold periods. The IPO induced changes are most pronounced during the boreal cold season. The results reinforce the notion that tropical Pacific SSTs (and the accompanying SST anomalies in the North Pacific) have large impacts on US precipitation and highlight the need to understand and simulate the IPO for decadal prediction of US precipitation. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to evaluate the Advanced Research Weather Research and Forecasting (WRF) regional model in simulating the precipitation over southern Africa during austral summer. The model's ability to reproduce the southern African mean climate and its variability around this mean state was evaluated by using the two-tier approach of specifying sea surface temperature (SST) to WRF and by using the one-tier approach of coupling the WRF with a simple mixed-layer ocean model. The boundary conditions provided by the reanalysis-II data were used for the simulations. Model experiments were conducted for twelve austral summers from DJF1998-99 to DJF2009-10. The experiments using both the two-tier and one-tier approaches simulated the spatial and temporal distributions of the precipitation realistically. However, both experiments simulated negative biases over Mozambique. Furthermore, analysis of the wet and dry spells revealed that the one-tier approach is superior to the two-tier approach. Based on the analysis of the surface temperature and the zonal wind shear it is noted that the simple mixed-layer ocean model coupled to WRF can be effectively used in place of two-tier WRF to simulate the climate of southern Africa. This is an important result because specification of SST at higher temporal resolutions in the subtropics is the most difficult task in the two-tier approach for most regional prediction models. The one-tier approach with the simple mixed-layer model can effectively reduce the complicacy of finding good SST predictions. [ABSTRACT FROM AUTHOR]

Surface temperatures are projected to increase 3-4°C over much of Africa by the end of the 21st century. Precipitation projections are less certain, but the most plausible scenario given by the Intergovernmental Panel on Climate Change (IPCC) is that the Sahel and East Africa will experience modest increases (~5%) in precipitation by the end of the 21st century. Evapotranspiration (E) is an important component of the water, energy, and biogeochemical cycles that impact several climate properties, processes, and feedbacks. The interaction of E with climate change drivers remains relatively unexplored in Africa. In this paper, we examine the trends in E, precipitation (P), daily maximum temperature (T), and daily minimum temperature (T) on a seasonal basis using a 31 year time series of variable infiltration capacity (VIC) land surface model (LSM) E. The VIC model captured the magnitude, variability, and structure of observed runoff better than other LSMs and a hybrid model included in the analysis. In addition, we examine the inter-correlations of E, P, T, and T to determine relationships and potential feedbacks. Unlike many IPCC climate change simulations, the historical analysis reveals substantial drying over much of the Sahel and East Africa during the primary growing season. In the western Sahel, large increases in daily maximum temperature appear linked to E declines, despite modest rainfall recovery. The decline in E and latent heating in this region could lead to increased sensible heating and surface temperature, thus establishing a possible positive feedback between E and surface temperature. [ABSTRACT FROM AUTHOR]

This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean-atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean-atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i.e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates. [ABSTRACT FROM AUTHOR]

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere-ocean-sea ice-land forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998-2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately. [ABSTRACT FROM AUTHOR]

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30-40 days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability. [ABSTRACT FROM AUTHOR]

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36 km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000-2005) from within the Tropics (30°S-30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36 km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations. [ABSTRACT FROM AUTHOR]

In this paper we use a coupled ocean-atmosphere model to investigate the impact of the interruption of Agulhas leakage of Indian ocean water on the tropical Atlantic, a region where strong coupled ocean-atmosphere interactions occur. The effect of a shut down of leakage of Indian ocean water is isolated from the effect of a collapse of the MOC. In our experiments, the ocean model is forced with boundary conditions in the southeastern corner of the domain that correspond to no interocean exchange of Indian ocean water into the Atlantic. The southern boundary condition is taken from the Levitus data and ensures an MOC in the Atlantic. Within this configuration, instead of warm and salty Indian ocean water temperature (cold) and salinity (fresh) anomalies of southern ocean origin propagate into the South Atlantic and eventually reach the equatorial region, mainly in the thermocline. This set up mimics the closure of the 'warm water path' in favor of the 'cold water path'. As part of the atmospheric response, there is a northward shift of the intertropical convergence zone (ITCZ). The changes in trade winds lead to reduced Ekman pumping in the equatorial region. This leads to a freshening and warming of the surface waters along the equator. Especially in the Cold Tongue region, the cold and fresh subsurface anomalies do not reach the surface due to the reduced upwelling. The anomaly signals are transported by the equatorial undercurrent and spread away from the equator within the thermocline. Part of the anomaly eventually reaches the Tropical North Atlantic, where it affects the Guinea Dome. Surprisingly, the main effect at the surface is small on the equator and relatively large at the Guinea Dome. In the atmosphere, the northward shift of the ITCZ is associated with a band of negative precipitation anomalies and higher salinities over the Tropical South Atlantic. An important implication of these results is that the modified water characteristics due to a shut down of the Agulhas leakage remain largely unaffected when crossing the equatorial Atlantic and therefore can affect the deepwater formation in the North Atlantic. This supports the hypothesis that the Agulhas leakage is an important source region for climate change and decadal variability of the Atlantic. [ABSTRACT FROM AUTHOR]

Recent studies have shown that changes in global mean precipitation are larger for solar forcing than for CO2 forcing of similar magnitude. In this paper, we use an atmospheric general circulation model to show that the differences originate from differing fast responses of the climate system. We estimate the adjusted radiative forcing and fast response using Hansen’s “fixed-SST forcing” method. Total climate system response is calculated using mixed layer simulations using the same model. Our analysis shows that the fast response is almost 40% of the total response for few key variables like precipitation and evaporation. We further demonstrate that the hydrologic sensitivity, defined as the change in global mean precipitation per unit warming, is the same for the two forcings when the fast responses are excluded from the definition of hydrologic sensitivity, suggesting that the slow response (feedback) of the hydrological cycle is independent of the forcing mechanism. Based on our results, we recommend that the fast and slow response be compared separately in multi-model intercomparisons to discover and understand robust responses in hydrologic cycle. The significance of this study to geoengineering is discussed. [ABSTRACT FROM AUTHOR]

This paper introduces an original method for climate change detection, called temporal optimal detection method. The method consists in searching for a smooth temporal pattern in the observations. This pattern can be either the response of the climate system to a specific forcing or to a combination of forcings. Many characteristics of this new method are different from those of the classical “optimal fingerprint” method. It allows to infer the spatial distribution of the detected signal, without providing any spatial guess pattern. The spatial properties of the internal climate variability doesn’t need to be estimated either. The estimation of such quantities being very challenging at regional scale, the proposed method is particularly well-suited for such scale. The efficiency of the method is illustrated by applying it on real homogenized datasets of temperatures and precipitation over France. A multimodel detection is performed in both cases, using an ensemble of atmosphere-ocean general circulation models for estimating the temporal patterns. Regarding temperatures, new results are highlighted, especially by showing that a change is detected even after removing the uniform part of the warming. The sensitivity of the method is discussed in this case, relatively to the computation of the temporal patterns and to the choice of the model. The method also allows to detect a climate change signal in precipitation. This change impacts the spatial distribution of the precipitation more than the mean over the domain. The ability of the method to provide an estimate of the spatial distribution of the change following the prescribed temporal patterns is also illustrated. [ABSTRACT FROM AUTHOR]

A number of general circulation model studies have assessed the impact of degradation of the land surface in the Sahel, mostly with idealized degradation scenarios. This paper builds on the previous research by testing the sensitivity of Mesoscale Convective Systems (MCS) and associated rainfall amounts to observed vegetation changes using a regional atmospheric model. Over the last 20 years, the vegetation in the Sahel has recovered from the drought in the 1980s and vegetation cover values have increased up to 20%. The sensitivity for both a vegetation increase and a decrease by these realistic amounts is investigated. The model simulations span 42 days of the rainy season and are centred over the region of the Hydrological and Atmospheric Pilot Experiment in the Sahel (HAPEX-Sahel), of which the data are used to evaluate model results. The model is able to correctly reproduce rainfall amounts and atmospheric profiles. Total precipitation is found to be insensitive to the applied vegetation changes, but the latter do have an impact on the rainfall patterns and the location of MCS. The model results indicate that the change in vegetation cover influences the MCS in two different ways: Firstly, the vegetation change is found to affect the surface fluxes and this in turn is found to affect the Convective Available Potential Energy (CAPE) and thereby the strength of the convective systems. The relation between vegetation cover and CAPE turns out to be affected by the time in-between precipitation events. Secondly, a change in atmospheric dynamics, especially the mid-tropospheric zonal flow, is modelled as response to a change in the spatial temperature and humidity distribution. Both mechanisms are likely to play a role in determining the characteristics of the rainfall pattern. [ABSTRACT FROM AUTHOR]

Climate models suggest that anthropogenic emissions are likely to induce an important drying during summer over most of Europe in the late 21st century. However, the amplitude of the associated decrease in precipitation strongly varies among the different climate models. In order to reduce this spread, it is first necessary to identify its causes and the associated physical mechanisms. Consequently, the focus of this paper is to better estimate the role of large scale circulation (LSC) in precipitation changes over Europe using a multi-model framework and then to characterize the LSC changes using the weather regime paradigm. We show that LSC changes directly lead to a decrease of precipitation over northwestern Europe. This circulation-driven decrease in rainfall is mainly linked to an increase (decrease) of the occurrence of positive (negative) phase of the North Atlantic Oscillation regime. LSC is also responsible for a significant part of the models spread in precipitation changes over these regions. Over southern Europe, the role of LSC changes on multi-model mean precipitation changes is generally weak. We also show that the precipitation anomalies directly induced by LSC modifications seem to be further amplified through local feedbacks. [ABSTRACT FROM AUTHOR]

This paper analyzes the soil–atmosphere feedbacks and uncertainties under current (1960–1990) and plausible future climate conditions (2070–2100, using the A2 greenhouse gases emission scenario). For this purpose, two vegetation descriptions differing only in the grassland and grass-with-trees proportion in some parts of the domain have been created. The combination of these two different climate scenarios and two vegetation descriptions defines four different 30-year experiments, which have been completed using a regional climate model. The domain is centered around the Mediterranean basin and covers most of Europe. The study focuses on the summer season when there are major differences between the two vegetation descriptions and when the impact of land–surface processes on precipitation is largest. Present climate experiments show large evapotranspiration differences over areas where vegetation changes have taken place. Precipitation increases (up to 3 mm day−1 in some regions) follow evapotranspiration increases, although with a more complex spatial structure. These results indicate a high sensitivity at regional scales of summer precipitation processes to vegetation changes. Future climate simulations show very similar changes to those observed in the current climate experiments. This indicates that the impacts of climate change are relatively independent to the land-cover descriptions used in this study. [ABSTRACT FROM AUTHOR]

The aim of this work is to assess potential future Antarctic surface mass balance changes, the underlying mechanisms, and the impact of these changes on global sea level. To this end, this paper presents simulations of the Antarctic climate for the end of the twentieth and twenty-first centuries. The simulations were carried out with a stretched-grid atmospheric general circulation model, allowing for high horizontal resolution (60 km) over Antarctica. It is found that the simulated present-day surface mass balance is skilful on continental scales. Errors on regional scales are moderate when observed sea surface conditions are used; more significant regional biases appear when sea surface conditions from a coupled model run are prescribed. The simulated Antarctic surface mass balance increases by 32 mm water equivalent per year in the next century, corresponding to a sea level decrease of 1.2 mm year−1 by the end of the twenty-first century. This surface mass balance increase is largely due to precipitation changes, while changes in snow melt and turbulent latent surface fluxes are weak. The temperature increase leads to an increased moisture transport towards the interior of the continent because of the higher moisture holding capacity of warmer air, but changes in atmospheric dynamics, in particular off the Antarctic coast, regionally modulate this signal. [ABSTRACT FROM AUTHOR]

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes. [ABSTRACT FROM AUTHOR]