Global Coupled Climate Model Research Articles

Synchrony between reanalysis-driven RCM simulations and observations: variation with time scale

Unlike coupled global climate models (CGCMs) that run in a stand-alone mode, nested regional climate models (RCMs) are driven by either a CGCM or a reanalysis dataset. This feature makes high correlations between the RCM simulation and its driver possible. When the driving dataset is a reanalysis, time correlations between RCM output and observations are also common and to be expected. In certain situations time correlation between driver and driven RCM is of particular interest and techniques have been developed to increase it (e.g. large-scale spectral nudging). For such cases, a question that remains open is whether aggregating in time increases the correlation between RCM output and observations. That is, although the RCM may be unable to reproduce a given daily event, whether it will still be able to satisfactorily simulate an anomaly on a monthly or annual basis. This is a preconception that the authors of this work and others in the community have held, perhaps as a natural extension of the properties of upscaling or aggregating other statistics such as the mean squared error. Here we explore analytically four particular cases that help us partially answer this question. In addition, we use observations datasets and RCM-simulated data to illustrate our findings. Results indicate that time upscaling does not necessarily increase time correlations, and that those interested in achieving high monthly or annual time correlations between RCM output and observations may have to do so by increasing correlation as much as possible at the shortest time scale. This may indicate that even when only concerned with time correlations at large temporal scale, large-scale spectral nudging acting at the time-step level may have to be used.

3-Step dynamical downscaling with empirical correction of sea-surface conditions: application to a CORDEX Africa simulation

Dynamical downscaling of climate projections over a limited-area domain using a Regional Climate Model (RCM) requires boundary conditions (BC) from a Coupled Global Climate Model (CGCM) simulation. Biases in CGCM-generated BC can have detrimental effects in RCM simulations, so attempts to improve the BC used to drive the RCM simulations are worth exploring. It is in this context that an empirical method involving the bias correction of the sea-surface conditions (SSCs; sea-surface temperature and sea-ice concentration) simulated by a CGCM has been developed: The 3-step dynamical downscaling approach. The SSCs from a CGCM simulation are empirically corrected and used as lower BC over the ocean for an atmosphere-only global climate model (AGCM) simulation, which in turn provides the atmospheric lateral BC to drive the RCM simulation. We analyse the impact of this strategy on the simulation of the African climate, with a special attention to the West African Monsoon (WAM) precipitation, using the fifth-generation Canadian Regional Climate Model (CRCM5) over the CORDEX-Africa domain. The Earth System Model of the Max-Planck-Institut für Meteorologie (MPI-ESM-LR) is used as CGCM and a global version of CRCM5 is used as AGCM. The results indicate that the historical climate is much improved, approaching the skill of reanalysis-driven hindcast simulations. The most remarkable effect of this approach is the positive impact on the simulation of all aspects of the WAM precipitation, mainly due to the correction of SSCs. In fact, our results show that proper sea surface temperature (SST) in the Gulf of Guinea is a necessary condition for an adequate simulation of WAM precipitation, especially over the equatorial region of West Africa. It was found that the climate-change projections under RCP4.5 scenario obtained with the 3-step approach are substantially different from those obtained with usual downscaling approach in which the RCM is directly driven by the CGCM output; in the WAM region most of the differences in the projected climate changes came mainly from the empirical correction of SST.

Roles of land surface albedo and horizontal resolution on the Indian summer monsoon biases in a coupled ocean–atmosphere tropical-channel model

The Indian summer monsoon (ISM) simulated over the 1989–2009 period with a new 0.75° ocean–atmosphere coupled tropical-channel model extending from 45°S to 45°N is presented. The model biases are comparable to those commonly found in coupled global climate models (CGCMs): the Findlater jet is too weak, precipitations are underestimated over India while they are overestimated over the southwestern Indian Ocean, South-East Asia and the Maritime Continent. The ISM onset is delayed by several weeks, an error which is also very common in current CGCMs. We show that land surface temperature errors are a major source of the ISM low-level circulation and rainfall biases in our model: a cold bias over the Middle-East (ME) region weakens the Findlater jet while a warm bias over India strengthens the monsoon circulation over the southern Bay of Bengal. A surface radiative heat budget analysis reveals that the cold bias is due to an overestimated albedo in this desertic ME region. Two new simulations using a satellite-observed land albedo show a significant and robust improvement in terms of ISM circulation and precipitation. Furthermore, the ISM onset is shifted back by 1 month and becomes in phase with observations. Finally, a supplementary set of simulations at 0.25°-resolution confirms the robustness of our results and shows an additional reduction of the warm and dry bias over India. These findings highlight the strong sensitivity of the simulated ISM rainfall and its onset timing to the surface land heating pattern and amplitude, especially in the ME region. It also illustrates the key-role of land surface processes and horizontal resolution for improving the ISM representation, and more generally the monsoons, in current CGCMs.

Atlantic near‐term climate variability and the role of a resolved Gulf Stream

AbstractThere is a continually increasing demand for near‐term (i.e., lead times up to a couple of decades) climate information. This demand is partly driven by the need to have robust forecasts and is partly driven by the need to assess how much of the ongoing climate change is due to natural variability and how much is due to anthropogenic increases in greenhouse gases or other external factors. Here we discuss results from a set of state‐of‐the‐art climate model experiments in comparison with observational estimates that show that an assessment of predictability requires models that capture the variability of major oceanic fronts, which are, at best, poorly resolved and may even be absent in the near‐term prediction of Intergovernmental Panel on Climate Change class models. This is the first time that air‐sea interactions associated with resolved Gulf Stream sea surface temperature have been identified in the context of a state‐of‐the‐art global coupled climate model with inferred near‐term predictability.

Evaluating and improving cloud phase in the Community Atmosphere Model version 5 using spaceborne lidar observations

AbstractSpaceborne lidar observations from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite are used to evaluate cloud amount and cloud phase in the Community Atmosphere Model version 5 (CAM5), the atmospheric component of a widely used state‐of‐the‐art global coupled climate model (Community Earth System Model). By embedding a lidar simulator within CAM5, the idiosyncrasies of spaceborne lidar cloud detection and phase assignment are replicated. As a result, this study makes scale‐aware and definition‐aware comparisons between model‐simulated and observed cloud amount and cloud phase. In the global mean, CAM5 has insufficient liquid cloud and excessive ice cloud when compared to CALIPSO observations. Over the ice‐covered Arctic Ocean, CAM5 has insufficient liquid cloud in all seasons. Having important implications for projections of future sea level rise, a liquid cloud deficit contributes to a cold bias of 2–3°C for summer daily maximum near‐surface air temperatures at Summit, Greenland. Over the midlatitude storm tracks, CAM5 has excessive ice cloud and insufficient liquid cloud. Storm track cloud phase biases in CAM5 maximize over the Southern Ocean, which also has larger‐than‐observed seasonal variations in cloud phase. Physical parameter modifications reduce the Southern Ocean cloud phase and shortwave radiation biases in CAM5 and illustrate the power of the CALIPSO observations as an observational constraint. The results also highlight the importance of using a regime‐based, as opposed to a geographic‐based, model evaluation approach. More generally, the results demonstrate the importance and value of simulator‐enabled comparisons of cloud phase in models used for future climate projection.

Decadal predictions with the HiGEM high resolution global coupled climate model: description and basic evaluation

This paper describes the development and basic evaluation of decadal predictions produced using the HiGEM coupled climate model. HiGEM is a higher resolution version of the HadGEM1 Met Office Unified Model. The horizontal resolution in HiGEM has been increased to 1.25^{circ } times 0.83^{circ } in longitude and latitude for the atmosphere, and 1/3^{circ } times 1/3^{circ } globally for the ocean. The HiGEM decadal predictions are initialised using an anomaly assimilation scheme that relaxes anomalies of ocean temperature and salinity to observed anomalies. 10 year hindcasts are produced for 10 start dates (1960, 1965,..., 2000, 2005). To determine the relative contributions to prediction skill from initial conditions and external forcing, the HiGEM decadal predictions are compared to uninitialised HiGEM transient experiments. The HiGEM decadal predictions have substantial skill for predictions of annual mean surface air temperature and 100 m upper ocean temperature. For lead times up to 10 years, anomaly correlations (ACC) over large areas of the North Atlantic Ocean, the Western Pacific Ocean and the Indian Ocean exceed values of 0.6. Initialisation of the HiGEM decadal predictions significantly increases skill over regions of the Atlantic Ocean, the Maritime Continent and regions of the subtropical North and South Pacific Ocean. In particular, HiGEM produces skillful predictions of the North Atlantic subpolar gyre for up to 4 years lead time (with ACC > 0.7), which are significantly larger than the uninitialised HiGEM transient experiments.

Climate change projections for the Texas High Plains and Rolling Plains

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

Analyses of Observed and Anticipated Changes in Extreme Climate Events in the Northwest Himalaya

In this study, past (1970-2005) as well as future long term (2011-2099) trends in various extreme events of temperature and precipitation have been investigated over selected hydro-meteorological stations in the Sutlej river basin. The ensembles of two Coupled Model Intercomparison Project (CMIP3) models: third generation Canadian Coupled Global Climate Model and Hadley Centre Coupled Model have been used for simulation of future daily time series of temperature (maximum and minimum) and precipitation under A2 emission scenario. Large scale atmospheric variables of both models and National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis data sets have been downscaled using statistical downscaling technique at individual stations. A total number of 25 extreme indices of temperature (14) and precipitation (11) as specified by the Expert Team of the World Meteorological Organization and Climate Variability and Predictability are derived for the past and future periods. Trends in extreme indices are detected over time using the modified Mann-Kendall test method. The stations which have shown either decrease or no change in hot extreme events (i.e., maximum TMax, warm days, warm nights, maximum TMin, tropical nights, summer days and warm spell duration indicators) for 1970–2005 and increase in cold extreme events (cool days, cool nights, frost days and cold spell duration indicators) are predicted to increase and decrease respectively in the future. In addition, an increase in frequency and intensity of extreme precipitation events is also predicted.

Observing climate change trends in ocean biogeochemistry: when and where.

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change‐driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long‐term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long‐term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (<~30 years) to detect trends than other areas. In addition, we quantify the ‘footprint’ of existing and planned time series stations, that is the area over which a station is representative of a broader region. Footprints are generally largest for pH and sea surface temperature, but nevertheless the existing network of observatories only represents 9–15% of the global ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change‐driven responses in the marine ecosystem.

Arctic Sea Ice: Decadal Simulations and Future Scenarios Using BESM-OA

Important international reports and a significant number of scientific publications have reported on the abrupt decline of Arctic sea ice and its impact on the Global Climate System. In this paper, we evaluated the ability of the newly implemented Brazilian Earth System Model (BESM-OA) to represent Arctic sea ice and sensitivity to CO2 forcing, using decadal simulations (1980-2012) and future scenarios (2006-2100). We validated our results with satellite observations and compared them to Coupled Model Intercomparison Project, Phase 5 (CMIP5) for the same numerical experiment. BESM results for the seasonal cycle are consistent with CMIP5 models and observations. However, almost all models tend to overestimate sea ice extent in March compared to observations. The correct evaluation of minimum record of sea ice, in terms of time, spatial and area remains a limitation in Coupled Global Climate Models. Looking to spatial patterns, we found a systematic model error in September sea ice cover between the Beaufort Sea and East Siberia for most models. Future scenarios show a decrease in sea ice extent in response to an increase in radiative forcing for all models. From the year 2045 onwards, all models show a dramatic shrinking in sea ice and ice free conditions at the end of the melting season. The projected future sea ice loss is explained by the combined effects of the amplified warming in northern hemisphere high latitudes and feedbacks processes.

Assessment of Climate Change for Precipitation and Temperature Using Statistical Downscaling Methods in Upper Godavari River Basin, India

In the present study SDSM downscaling model was used as a tool for downscaling weather data statistically in upper Godavari river basin. Two Global Climate Models (GCMs), CGCM3 and HadCM3, have been used to project future maximum temperature (Tmax), minimum temperature (Tmin) and precipitation. The predictor variables are extracted from: 1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1961-2003, 2) the simulations from the third-generation Hadlycentre Coupled Climate Model (HadCM3) and Coupled Global Climate Model (CGCM3) variability and changes in Tmax, Tmin and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model have been presented for future periods: 2020s, 2050s and 2080s. The scatter-plots and cross-correlations are used for verifying the reliability of the simulation. Maximum temperature increases in future for almost all the scenarios for both GCMs. Also downscaled future precipitation shows increasing trends for all scenarios.

Toward Quantifying the Increasing Role of Oceanic Heat in Sea Ice Loss in the New Arctic

Abstract The loss of Arctic sea ice has emerged as a leading signal of global warming. This, together with acknowledged impacts on other components of the Earth system, has led to the term “the new Arctic.” Global coupled climate models predict that ice loss will continue through the twenty-first century, with implications for governance, economics, security, and global weather. A wide range in model projections reflects the complex, highly coupled interactions between the polar atmosphere, ocean, and cryosphere, including teleconnections to lower latitudes. This paper summarizes our present understanding of how heat reaches the ice base from the original sources—inflows of Atlantic and Pacific Water, river discharge, and summer sensible heat and shortwave radiative fluxes at the ocean/ice surface—and speculates on how such processes may change in the new Arctic. The complexity of the coupled Arctic system, and the logistic and technological challenges of working in the Arctic Ocean, require a coordinated interdisciplinary and international program that will not only improve understanding of this critical component of global climate but will also provide opportunities to develop human resources with the skills required to tackle related problems in complex climate systems. We propose a research strategy with components that include 1) improved mapping of the upper- and middepth Arctic Ocean, 2) enhanced quantification of important process, 3) expanded long-term monitoring at key heat-flux locations, and 4) development of numerical capabilities that focus on parameterization of heat-flux mechanisms and their interactions.

Low-frequency variability of surface air temperature over the Barents Sea: causes and mechanisms

The predominant decadal to multidecadal variability in the Arctic region is a feature that is not yet well-understood. It is shown that the Barents Sea is a key region for Arctic-wide variability. This is an important topic because low-frequency changes in the ocean might lead to large variations in the sea-ice cover, which then cause massive changes in the ocean-atmosphere heat exchanges. Here we describe the mechanism driving surface temperatures and heat fluxes in the Barents Sea based primarily on analyzes of one global coupled climate model. It is found that the ocean drives the low-frequency changes in surface temperature, whereas the atmosphere compensates the oceanic transport anomalies. The seasonal dependence and the role of individual components of the ocean-atmosphere energy budget are analyzed in detail, showing that seasonally-varying climate mechanisms play an important role. Herein, sea ice is governing the seasonal response, by acting as a lid that opens and closes during warm and cold periods, respectively, thereby modulating the surface heat fluxes.

An improvement in mass flux convective parameterizations and its impact on seasonal simulations using a coupled model

A new closure and a modified detrainment for the simplified Arakawa–Schubert (SAS) cumulus parameterization scheme are proposed. In the modified convective scheme which is named as King Abdulaziz University (KAU) scheme, the closure depends on both the buoyancy force and the environment mean relative humidity. A lateral entrainment rate varying with environment relative humidity is proposed and tends to suppress convection in a dry atmosphere. The detrainment rate also varies with environment relative humidity. The KAU scheme has been tested in a single column model (SCM) and implemented in a coupled global climate model (CGCM). Increased coupling between environment and clouds in the KAU scheme results in improved sensitivity of the depth and strength of convection to environmental humidity compared to the original SAS scheme. The new scheme improves precipitation simulation with better representations of moisture and temperature especially during suppressed convection periods. The KAU scheme implemented in the Seoul National University (SNU) CGCM shows improved precipitation over the tropics. The simulated precipitation pattern over the Arabian Peninsula and Northeast African region is also improved.

Nonlinear Zonal Wind Response to ENSO in the CMIP5 Models: Roles of the Zonal and Meridional Shift of the ITCZ/SPCZ and the Simulated Climatological Precipitation*

Abstract The observed equatorial Pacific zonal wind response during El Niño tends to be stronger than during La Niña. Most global coupled climate models in phase 5 of CMIP (CMIP5) exhibit such nonlinearity, although weaker than observed. The wind response nonlinearity can be reproduced by driving a linear shallow water atmospheric model with a model’s or the observed precipitation anomalies, which can be decomposed into two main components: the zonal and meridional redistribution of the climatological precipitation. Both redistributions contribute comparably to the total rainfall anomalies, whereas the zonal redistribution plays the dominant role in the zonal wind response. The meridional redistribution component plays an indirect role in the nonlinear wind response by limiting the zonal redistribution during La Niña and thus enhancing the nonlinearity in the wind response significantly. During La Niña, the poleward movement of the ITCZ/SPCZ reduces the equatorial zonal-mean precipitation available for the zonal redistribution and its resulting zonal wind response. Conversely, during El Niño, the equatorward movement of the ITCZ and SPCZ do not limit the zonal redistribution of precipitation. The linear equatorial zonal wind response to ENSO is found to have a significant linear correlation with the equatorial central Pacific climatological precipitation and SST among the CMIP5 models. However, no linear correlation is found between the nonlinear equatorial zonal wind response and the climatological precipitation.

The Impacts of Climate Change on the Autumn North Atlantic Wave Climate

In this study, we investigate the impact of global warming induced by possible climate change on the autumn winds, the related storm climate, and the wave climate over the North Atlantic Ocean. These analyses are based on a third-generation wave model, WAVEWATCHIII™ and dynamically downscaled winds, obtained from the Canadian Regional Climate Model driven by the third version of the Coupled Global Climate Model (T47) from the Canadian Centre for Climate Modelling and Analysis following the A1B climate change scenario of the Special Report on Emission Scenarios from the Intergovernmental Panel on Climate Change. Compared with the present wave climate, represented as 1970–1999, the significant wave heights in the northeast North Atlantic will increase, whereas in other areas, such as the mid-latitudes, they will decrease, with associated changes in winds in the future climate (2040–2069). An analysis of inverse wave ages is used to suggest that wind-driven wave regimes tend to occur more frequently in the northeast North Atlantic and decrease in the mid-latitudes in the climate change scenario. The dominant North Atlantic storm-track region is estimated to shift northward, especially over the northern Northeast Atlantic, where the frequency of occurrence of the most intense cyclones is estimated to increase. We suggest that changes in storm densities are related to changes in the upper level steering flow in the atmosphere, which are the precursor to changes in the winds and ocean waves.

Vulnerability of Water Demand and Aquatic Habitat in the Context of Climate Change and Analysis of a No-Regrets Adaptation Strategy: Study of the Yamaska River Basin, Canada

AbstractClimate change will have a significant impact on the hydrological cycle. This paper presents the results of a pilot project for the Yamaska River in Quebec. The objective of this project is to evaluate the river’s vulnerability to low flows attributable to climate change and to analyze a no-regrets adaptation strategy at locations identified as vulnerable. The vulnerability was evaluated using statistical indicators (low flow indices) based on long-term observations at four locations in the basin. A distributed physically-based hydrological model in use in Quebec was calibrated and validated against observed streamflow data to properly represent low flows. Hydrological simulations used seven climate projections provided by the north american regional climate change assessment program (NARCCAP) s project. Also, five members of the canadian regional climate model (CRCM), nested with the coupled global climate model (CGCM) under the special report on emission scenarios (SRES) A2 emission scenario, we...

El Niño and Indian summer monsoon rainfall relationship in retrospective seasonal prediction runs: experiments with coupled global climate models and MMEs

The relationship between the warm phase of El Nino southern oscillation (ENSO) and Indian summer monsoon rainfall is explored through seven coupled global climate models (CGCMs), which are semi-operational at APEC Climate Center (APCC). The 23-year (1983–2005) hindcast datasets of individual model ensembles derived from May initial conditions for southwest monsoon season (JJAS) are utilized to find out the simultaneous influence of El Nino–ISMR relationship in 1990s, which is observed to be weaker than present decades. The hindcast of ISMR climatology derived from seven individual models viz. APCC, NCEP, POAMA, SINT, SUT1, PNU and UHT1 appears to be reasonably simulated; in particular, about 50 % of mean departure is evident in most CGCMs. In addition, four of six El Nino years during the aforementioned period are well depicted in most of the CGCMs, while the years 1994 and 1997 are not represented well by these seven individual models. The warm SST anomaly aligned with surplus precipitation over tropical equatorial Pacific region simulated using APCC, NCEP, POAMA, SINT and SUT1 is relatively better than that simulated in PNU and UHT1 and it is closer to observation. The El Nino–ISMR teleconnection skills both monthly to seasonal scale are very poor in PNU as well as UHT1 and their RMSEs are 3.84 and 3.77 higher than APCC, NCEP, POAMA, SINT and SUT1 models. The authors developed two Multi-Model Ensembles (MMEs) that were simple composites of ensemble forecast from seven models (APCC, NCEP, POAMA, SINT, SUT1, PNU and UHT1) referred to as MME1, and from five models (APCC, NCEP, POAMA, SINT and SUT1) are referred to as MME2. Importantly, the one-month lead MME2 prediction of anomaly correlation coefficient (ACC) and its adverse impacts is reasonably better than MME1 prediction. However, there are some limitations in capturing SST forcing fields over Indian Ocean region in both MMEs. Among the seven models, SINT has the highest pattern correlation of precipitation over the Indian monsoon region.

Scenario Changes of Atlantic Water in the Arctic Ocean

Abstract The authors explore possible temperature modifications of the Atlantic Water Layer (AWL) induced by climate change, performing simulations for 1970 to 2099 with a coupled ice–ocean Arctic model (CIOM). Surface fields to drive the CIOM were provided by the Canadian Regional Climate Model (CRCM), driven by outputs from the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) following the A1B climate change scenario. In the present climate, represented as 1990–2009, the CIOM can reliably reproduce the AWL compared to Polar Science Center Hydrographic Climatology (PHC) data. For the future climate, assuming the A1B climate change scenario, there is a significant increase in water volume transport into the central Arctic Ocean through Fram Strait due to the weakened atmospheric high pressure system over the western Arctic and an intensified atmospheric low pressure system over the Nordic seas. The AWL temperature tends to decrease from 0.36°C in the 2010s to 0.26°C in the 2060s. In the vertical, the warm Atlantic water core slightly expands before the 2030s, significantly shrinks after the 2050s, and essentially disappears by 2070–99, in the southern Beaufort Sea. The temperature decrease after 2030 is mainly due to the reduced heat fluxes in the Kara and Barents Seas. In the northeastern Barents and Kara Seas, the loss of sea ice increases the heat loss from the Atlantic water and reduces the water temperature near the bottom, contributing to decreased heat fluxes into the central Arctic Ocean, as well as decreased AWL temperature at central Arctic Ocean intermediate layers. In addition, the vertically integrated heat loss also plays an important role in the AWL cooling process.

Teleconnection responses in multi-GCM driven CORDEX RCMs over Eastern Africa

The ability of climate models to simulate atmospheric teleconnections provides an important basis for the use and analysis of climate change projections. This study examines the ability of COordinated Regional climate Downscaling EXperiment models, with lateral and surface boundary conditions derived from Coupled Global Climate Models (CGCMs), to simulate the teleconnections between tropical sea surface temperatures and rainfall over Eastern Africa. The ability of the models to simulate the associated changes in atmospheric circulation patterns over the region is also assessed. The models used in the study are Rossby Centre regional atmospheric model (RCA) driven by eight CGCMs and COnsortium for Small scale MOdeling (COSMO) Climate Limited-area Modelling (COSMO-CLM or CCLM) driven by four of the same CGCMs. Teleconnection patterns are examined using correlation, regression and composite analysis. In order to identify the source of the errors, CGCM-driven regional climate model (RCM) results are compared with ERA-Interim driven RCM results. Results from the driving CGCMs are also analyzed. The RCMs driven by reanalysis (quasi-perfect boundary conditions) successfully capture rainfall teleconnections in most examined regions and seasons. Our analysis indicates that most of the errors in simulating the teleconnection patterns come from the driving CGCMs. RCMs driven by MPI-ESM-LR, HadGEM2-ES and GFDL-ESM2M tend to perform relatively better than RCMs driven by other CGCMs. CanESM2 and MIROC5, and their corresponding downscaled results capture the teleconnections in most of the sub-regions and seasons poorly. This highlights the relative importance of CGCM-derived boundary conditions in the downscaled product and the need to improve these as well as the RCMs themselves. Overall, the results produced here will be very useful in identifying and selecting CGCMs and RCMs for the use of climate change projecting over the Eastern Africa.