CMIP5 General Circulation Models Research Articles

Pseudo-climate modelling study on projected changes in extreme extratropical cyclones, storm waves and surges under CMIP5 multi-model ensemble: Baltic Sea perspective

In order to estimate the possible parameters of future extreme extratropical cyclones (ETCs), a pseudo-climate modelling study of three historical storms originating from the Atlantic Ocean and one from the Black Sea area was performed using multi-model approach considering IPCC emission scenarios RCP4.5 and RCP8.5 for the twenty-first century. Applying Weather Research and Forecasting atmosphere model (WRF), Finite Volume Community Ocean model (FVCOM-SWAVE) and the Simulating WAves Nearshore (SWAN) model, the changes in initial conditions in atmospheric air temperature, sea surface temperature and relative humidity were considered on the basis of 14 CMIP5 general circulation models ensemble. According to the future scenario results, no notable changes are expected in minimum atmospheric pressure within the ETCs of the future; however, the low pressure area was slightly larger and the strong wind zone was extending further south with greater peak wind speeds in the future (year 2081–2100) simulations. This, in turn, yielded a small surge height increase at Parnu under RCP4.5 scenario; however, under RCP8.5 scenario the surge increase was up to 22–59 cm. Westerly approaching ETCs will bring more precipitation to the Baltic Sea area in the (warmer) future. In case of a southerly cyclone, the results were more mixed. An insignificant increase in wave heights during extreme storm conditions occurred. Although RCP8.5 future scenario is usually considered as unrealistic, the results of this study still suggest that the extreme ETCs may become more dangerous in the future, although probably not as certainly as tropical cyclones.

Climate projections for glacier change modelling over the Himalayas.

Glaciers are of key importance to freshwater supplies in the Himalayan region. Their growth or decline is among other factors determined by an interaction of 2‐m air temperature (TAS) and precipitation rate (PR) and thereof derived positive degree days (PDD) and snow and ice accumulation (SAC). To investigate determining factors in climate projections, we use a model ensemble consisting of 36 CMIP5 general circulation models (GCMs) and 13 regional climate models (RCMs) of two Asian CORDEX domains for two different representative concentration pathways (RCP4.5 and RCP8.5). First, we downsize the ensemble in respect to the models' ability to correctly reproduce dominant circulation patterns (i.e., the Indian summer monsoon [ISM] and western disturbances [WDs]) as well as elevation‐dependent trend signals in winter. Within this evaluation, a newly produced data set for the Indus, Ganges and Brahmaputra catchments is used as observational data. The reanalyses WFDEI, ERA‐Interim, NCEP/NCAR and JRA‐55 are used to further account for observational uncertainty. In a next step, remaining TAS and PR data are bias corrected applying a new bias adjustment method, scale distribution mapping, and subsequently PDD and SAC computed. Finally, we identify and quantify projected climate change effects. Until the end of the century, the ensemble indicates a rise of PDD, especially during summer and for lower altitudes. Also TAS is rising, though the highest increases are shown for higher altitudes and between December and April (DJFMA). PRs connected to the ISM are projected to robustly increase, while signals for PR changes during DJFMA show a higher level of uncertainty and spatial heterogeneity. However, a robust decline in solid precipitation is projected over our research domain, with the exception of a small area in the high mountain Indus catchment where no clear signal emerges.

Precipitation projection using a CMIP5 GCM ensemble model: a regional investigation of Syria

The possible changes in precipitation of Syrian due to climate change are projected in this study. The symmetrical uncertainty (SU) and multi-criteria decision-analysis (MCDA) methods are used to identify the best general circulation models (GCMs) for precipitation projections. The effectiveness of four bias correction methods, linear scaling (LS), power transformation (PT), general quantile mapping (GEQM), and gamma quantile mapping (GAQM) is assessed in downscaling GCM simulated precipitation. A random forest (RF) model is performed to generate the multi model ensemble (MME) of precipitation projections for four representative concentration pathways (RCPs) 2.6, 4.5, 6.0, and 8.5. The results showed that the best suited GCMs for climate projection of Syria are HadGEM2-AO, CSIRO-Mk3-6-0, NorESM1-M, and CESM1-CAM5. The LS demonstrated the highest capability for precipitation downscaling. Annual changes in precipitation is projected to decrease by −30 to −85.2% for RCPs 4.5, 6.0, and 8.5, while by < 0.0 to −30% for RCP 2.6. The precipitation is projected to decrease in the entire country for RCP 6.0, while increase in some parts for other RCPs during wet season. The dry season of precipitation is simulated to decrease by −12 to −93%, which indicated a drier climate for the country in the future.

Detection of UHI bias in China climate network using Tmin and Tmax surface temperature divergence

Near-surface temperature records show that China warmed by about 0.8 °C from 1950 to 2010. However, there exists an ongoing debate about whether this warming might have been partially due to urbanization bias. In fact, homogenization approaches may be inefficient in densely populated provinces that have experienced a significant urban development since the 1940s. This paper aims to complement previous research on the topic by showing that an alternative approach based on the analysis of the divergence between the minimum (Tmin) and maximum (Tmax) near-surface temperature records since the 1940s could be useful to clarify the issue because urban heat island (UHI) effects stress the warming of nocturnal temperatures more than the diurnal ones. Then, the significance of the divergence observed in the data could be evaluated against the expectations produced by the CMIP5 general circulation model simulations. From 1945–1954 to 2005–2014, on average and over China, these models predict that Tmin had to warm 0.19 ± 0.06 °C more than Tmax. However, during the same period, the climatic records show that Tmin warmed 0.83 ± 0.15 °C more than Tmax. A similar analysis demonstrates that the effect is more pronounced during the colder months from November–April than during the warmer ones from May to October. A comparison versus China urbanization records demonstrates that the regions characterized by a large Tmin-Tmax divergence are also the most densely populated ones, such as north-east China, that have experienced a diffused and fast urbanization since the 1940s. The results are significant and may indicate the presence of a substantial uncorrected urbanization bias in the Chinese climate records. Under the hypothesis that Tmax is a better metric for studying climatic changes than Tmean or Tmin, we conclude that about 50% of the recorded warming of China since the 1940s could be due to uncorrected urbanization bias. In addition, we also find that the Tmax record from May to October over China shows the 1940s and the 2000s equally warm, in contrast to the 1 °C warming predicted by the CMIP5 models.

Assessing landscape potential for human sustainability and ‘attractiveness’ across Asian Russia in a warmer 21st century

In the past, human migrations have been associated with climate change. As our civilizations developed, humans depended less on the environment, in particular on climate, because technological and economic development in the span of human history allowed us to adapt to and overcome environmental discomfort. Asian Russia (east of the Urals to the Pacific) is known to be sparsely populated. The population is concentrated along the forest-steppe in the south, with its comfortable climate and thriving agriculture on fertile soils. We use current and predicted climate scenarios to evaluate the climate comfort of various landscapes to determine the potential for human settlers throughout the 21st century. Climate change scenarios are taken from 20 CMIP5 general circulation models. Two CO2 Representative Concentration Pathway scenarios, RCP 2.6 representing mild climate change and RCP 8.5 representing more extreme changes, are applied to the large subcontinental territory of Asian Russia. The ensemble January and July temperature anomaly means and annual precipitation are calculated with respect to the baseline 1961–1990 climate. Three climate indices, which are important for human livelihood and well-being, are calculated based on January and July temperatures and annual precipitation: Ecological Landscape Potential, winter severity, and permafrost coverage. Climates predicted by the 2080s over Asian Russia would be much warmer and milder. Ensemble means do not show extreme aridity. The permafrost zone is predicted to significantly shift to the northeast. Ecological Landscape Potential would increase 1–2 categories from ‘low’ to ‘relatively high’ which would result in a higher capacity for population density across Asian Russia. Socio-economic processes and policy choices will compel the development that will lead to attracting people to migrate throughout the century. Therefore, understanding ecological landscape potential is crucial information for developing viable strategies for long-term economic and social development in preparation for climate migration and strategic adaptation planning.

Wave climate projections along the Indian coast

AbstractFuture changes in wave climate will influence the marine ecosystem, coastal erosion, design of coastal defences, operation of near‐ and off‐shore structures, and coastal zone management policies and may further add to the potential vulnerabilities of coastal regions to projected sea level rise. Many studies have reported changes in the global wave characteristics under climate change scenarios, but it is important to project future changes in local/regional wave climate for smooth implementation of policies and preventing severe coastal erosion and flooding. In this study the regional wave climate along the Indian coast for two time slices, 2011–2040 and 2041–2070, is reported using an ensemble of near‐surface winds generated by four different CMIP5 general circulation models (GCMs), under RCP4.5 scenario. Comparison of the wave climate for the two time slices shows an increase in wave heights and periods along much of the Indian coast, with the maximum wave heights increasing by more than 30% in some locations. An important finding is that at most locations along the east coast, wave periods are expected to increase by almost 20%, whereas along the west coast an increase of around 10% is expected. This will alter the distribution of wave energy at the shoreline through changes in wave refraction and diffraction, with potential implications for the performance and design of coastal structures and swash‐aligned beaches. Furthermore, the computations show material changes in the directional distribution of waves. This is particularly important in determining the longshore transport of sediments and can lead to realignment of drift‐aligned beaches, manifesting itself as erosion and/or siltation problems. This study is a preliminary contribution towards regional climate projections for the Indian Ocean region which are needed to plan and mitigate the impacts of future climate change.

Characterization of Air and Ground Temperature Relationships within the CMIP5 Historical and Future Climate Simulations

AbstractThe relationships between air and ground surface temperatures across North America are examined in the historical and future projection simulations from 32 general circulation models (GCMs) included in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). The difference between surface air (2 m) and ground surface (10 cm) temperatures is affected by simulated snow cover, vegetation cover, and precipitation by means of changes in soil moisture and soil properties. In winter, the differences between air and ground surface temperatures, for all CMIP5 simulations, are related to the insulating effect of snow cover and soil freezing phenomena. In summer, large leaf area index and large precipitation rates correspond to smaller differences between air and ground temperatures for the majority of simulations, likely due to induced changes in latent and sensible heat fluxes at the ground surface. Our results show that the representation of air‐ground coupling, analyzed using the difference between ground and air surface temperatures as metric, differs from observations, the North American Regional Reanalysis product and among the CMIP5 GCM simulations, by amounts that depend on the employed land surface model. The large variability among GCMs and the marked dependence of the results on the choice of the land surface model illustrate the need for improving the representation of processes controlling the coupling of the lower atmosphere and the land surface in GCMs as a mean of reducing the variability in their representation of weather and climate phenomena.

Leading interannual variability modes of East Asian winter precipitation in CMIP5 general circulation models

Leading interannual variability modes of East Asian winter precipitation in CMIP5 general circulation models

How well do climate models reproduce variability in observed rainfall? A case study of the Lake Victoria basin considering CMIP3, CMIP5 and CORDEX simulations

In this study, how well the climate models reproduce variability in observed rainfall was assessed based on General Circulation Models (GCMs) from phase 3 and phase 5 of the Coupled Model Inter-comparison Project, i.e., CMIP3 and CMIP5, respectively as well as the Regional Climate Models (RCMs) of COordinated Regional Climate Downscaling EXperiment (CORDEX) over Africa. Observed and climate model based daily rainfall across the Lake Victoria Basin, which is one of the wettest parts of Africa, was considered. Temporal variability was assessed based on the coefficient of variation of daily and annual rainfall, and the maximum dry and wet spell in each year. Furthermore, variation in daily rainfall was assessed in terms of the long-range dependence. Comparison of variability results from observed and climate model based rainfall was made. It was found that the capacity to reproduce variability in observed wet and dry conditions depends on the specific GCM (of CMIP3 or CMIP5) or CORDEX RCM. However, the CORDEX RCMs replicated variability in observed daily rainfall better than the CMIP3 and CMIP5 GCMs. This was due to the spatial resolutions of the CORDEX RCMs which are higher than those of the CMIP3 and CMIP5 GCMs. The ensemble mean of the coefficients of correlation between the variability in observed and that of climate model based rainfall was close to zero for both the GCMs or RCMs. This suggests that analyses can be done on a case by case basis. In other words, GCMs or RCMs which adequately reproduce variability in observed wet and dry conditions can be considered for further statistical analysis of the changes especially on the basis of statistical methods for downscaling. For daily timescale, both the GCMs and RCMs from all the three sets of climate models generally exhibited poor performance in capturing the time of occurrences and the magnitudes of rainfall events (when considered in a combined way). To reliably assess long-term rainfall changes, it is vital to characterize natural variation in terms of the statistical dependence. With respect to natural variability of rainfall at local scales, there is room for further improvement of the climate models; however, whether theory of fractals and/or concepts of scaling behavior or self-similarity can explicitly contribute in that respect is a crucial consideration. Results from this study gave some insights in the reasonableness of the future rainfall projections.

Data-driven ensemble model to statistically downscale rainfall using nonlinear predictor screening approach

The Artificial Intelligence (AI) models i.e., Artificial Neural Network (ANN) and Least Square Support Vector Machine (LSSVM) were used to statistically downscale and project rainfall data from CMIP5 General Circulation Models (GCMs) for Tabriz and Ardabil synoptic stations in north west Iran. Since one of the important issues in statistical downscaling of GCMs is to select dominant variables among plenty of large-scale climate data (predictors), a predictors screening framework, which integrates Wavelet-Entropy (WE) and Self-Organizing Map (SOM) was developed in this study to statistically downscale mean monthly rainfall values. The advantage of proposed method is to reduce the noise and dimensionality of data as well as selecting reliable inputs of downscaling model for future rainfall projection. To this end, five GCMs (i.e., Can-ESM2, BNU-ESM, CSIRO-ACCESS1, GFDL-ESM2G and INM-CM4) were employed. ANN, LSSVM and Multiple Linear Regression (MLR) models were trained to capture relationship between the predictors and the stations’ observed rainfall values (predictand). Then, ensemble techniques were applied on the outputs of the downscaling models. The calibration, validation and projection of the proposed downscaling models were performed over the periods; Jan. 1951 to Dec. 1991, Jan. 1992 to Dec. 2005 and Jan. 2017 to Dec. 2100, respectively. The projection of rainfall for near and distant future (i.e., 2017–2050 and 2050–2100) by the proposed multi-GCM ensemble framework yielded to rainfall alteration pattern; 40–41% and 35–42% decrease at Tabriz station and 6–12% and 5–13% increase at Ardabil station under RCPs 4.5 and 8.5, respectively.

Future climate and cryosphere impacts on the hydrology of a scarcely gauged catchment on the Jhelum river basin, Northern Pakistan

Streamflow projections are fundamental sources for future water resources strategic planning and management, particularly in high-altitude scarcely-gauged basins located in high mountain Asia. Therefore, quantification of the climate change impacts on major hydrological components (evapotranspiration, soil water storage, snowmelt-runoff, rainfall-runoff and streamflow) is of high importance and remains a challenge. For this purpose, we analysed general circulation models (GCMs) using a multiple bias correction approach and two different hydrological models i.e. the Hydrological Modelling System (HEC-HMS) and the Snowmelt Runoff Model (SRM), to examine the impact of climate change on the hydrological behaviour of the Jhelum River basin. Based on scrutiny, climate projections using four best fit CMIP5 GCMs (i.e. BCC-CSM1.1, INMCM4, IPSL-CM5A-LR and CMCC-CMS) were chosen by evaluating linear scaling, local intensity scaling (LOCI) and distribution mapping (DM) approaches at twenty climate stations. Subsequently, after calibration and validation of HEC-HMS and SRM at five streamflow gauging stations, the bias corrected projected climate data was integrated with HEC-HMS and SRM to simulate projected streamflow. Results demonstrate that the DM approach fitted the projections best. The climate projections exhibited maximum intra-annual rises in precipitation by 183.2 mm (12.74%) during the monsoon for RCP4.5 and a rise in Tmin (Tmax) by 4.77 °C (4.42 °C) during pre-monsoon, for RCP8.5 during 2090s. The precipitation and temperature rise is expected to expedite and increase snowmelt-runoff up to 48% and evapotranspiration and soil water storage up to 45%. The projections exhibited significant increases in streamflows by 330 m3/s (22.6%) for HEC-HMS and 449 m3/s (30.7%) for SRM during the pre-monfaf0000soon season by the 2090s under RCP8.5. Overall, our results reveal that the pre-monsoon season is potentially utmost affected under scenario-periods, and consequently, which has the potential to alter the precipitation and flow regime of the Jhelum River basin due to significant early snow- and glacier-melt.

Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

Abstract. Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 ∘C and the change for mean annual Tmin may increase from 0.3 to 4.1 ∘C. The individual result of the HadCM3 GCM has a good agreement with the ensemble mean result. HadCM3 from CMIP3 using A2a and B2a scenarios and canESM2 from CMIP5 GCMs under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by SDSM. The result from the two GCMs under five different scenarios agrees with the increasing direction of three climate variables (precipitation, Tmax and Tmin). The relative change of the downscaled mean annual precipitation ranges from 2.1 to 43.8 % while the change for mean annual Tmax and Tmin may increase in the range from 0.4 to 2.9 ∘C and from 0.3 to 1.6 ∘C respectively.

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

AbstractConventional bias correction is usually applied on a grid‐by‐grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two‐step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back‐transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA‐based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Climate Change Impact on Wheat Production in the Southern Great Plains of the US Using Downscaled Climate Data

Gradually developing climatic and weather anomalies due to increasing concentration of atmospheric greenhouse gases can pose threat to farmers and resource managers. There is a growing need to quantify the effects of rising temperature and changing climates on crop yield and assess impact at a finer scale so that specific adaptation strategies pertinent to that location can be developed. Our work aims to quantify and evaluate the influence of future climate anomalies on winter wheat (Triticum aestivum L.) yield under the Representative Concentration Pathways 6.0 and 8.5 using downscaled climate projections from different General Circulation Models (GCMs) and their ensemble. Marksim downscaled daily data of maximum (TMax) and minimum (TMin) air temperature, rainfall, and solar radiation (SRAD) from different Coupled Model Intercomparison Project GCMs (CMIP5 GCMs) were used to simulate the wheat yield in water and nitrogen limiting and non-limiting conditions for the future period of 2040-2060. The potential impact of climate changes on winter wheat production across Oklahoma was investigated. Climate change predictions by the downscaled GCMs suggested increase in air temperature and decrease in total annual rainfall. This will be really critical in a rainfed and semi-arid agro-ecological region of Oklahoma. Predicted average wheat yield during 2040-2060 increased under projected climate change, compared with the baseline years 1980-2014. Our results indicate that downscaled GCMs can be applied for climate projection scenarios for future regional crop yield assessment.

Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming

Severe river floods often result in huge economic losses and fatalities. Since 1980, almost 1500 such events have been reported in Europe. This study investigates climate change impacts on European floods under 1.5, 2, and 3 K global warming. The impacts are assessed employing a multi-model ensemble containing three hydrologic models (HMs: mHM, Noah-MP, PCR-GLOBWB) forced by five CMIP5 general circulation models (GCMs) under three Representative Concentration Pathways (RCPs 2.6, 6.0, and 8.5). This multi-model ensemble is unprecedented with respect to the combination of its size (45 realisations) and its spatial resolution, which is 5 km over the entirety of Europe. Climate change impacts are quantified for high flows and flood events, represented by 10% exceedance probability and annual maxima of daily streamflow, respectively. The multi-model ensemble points to the Mediterranean region as a hotspot of changes with significant decrements in high flows from −11% at 1.5 K up to −30% at 3 K global warming mainly resulting from reduced precipitation. Small changes (< ±10%) are observed for river basins in Central Europe and the British Isles under different levels of warming. Projected higher annual precipitation increases high flows in Scandinavia, but reduced snow melt equivalent decreases flood events in this region. Neglecting uncertainties originating from internal climate variability, downscaling technique, and hydrologic model parameters, the contribution by the GCMs to the overall uncertainties of the ensemble is in general higher than that by the HMs. The latter, however, have a substantial share in the Mediterranean and Scandinavia. Adaptation measures for limiting the impacts of global warming could be similar under 1.5 K and 2 K global warming, but have to account for significantly higher changes under 3 K global warming.

Can statistical downscaling improve consensus among CMIP5 models for Indian summer monsoon rainfall projections?

ABSTRACTThe projections of plausible changes in future climate using coarse scale General Circulation Models (GCMs) for non‐smooth climate variables, such as precipitation, possess limited skill and are often conflicting. The confidence in precipitation projections is further obscured due to the exclusion of inter‐model and natural internal variability. The present study investigates how far statistical downscaling can provide confidence in future projections while incorporating these major uncertainties under a moderate radiative forcing scenario. Here, we assess the consensus for future changes in Indian Summer Monsoon Rainfall (ISMR) from 20 CMIP5 GCMs and their statistically downscaled counterparts at 0.05° resolution for the 21st century. The Statistical Downscaling (SD) model is skillful in capturing the spatial variability of observed ISMR and shows significant improvement over host GCMs. GCMs show consistent but spatially inhomogeneous increase in future ISMR which intensify towards the end of the century. On the other hand, the downscaled outputs show spatially non‐uniform trends which intensify in their respective directions from near to long term. Both projections show high inter‐model inconsistencies. The multi‐model consensus among GCMs and SD across these in‐congruent models depicts inconclusive and highly uncertain changes in future. The GCMs show significant change with high model inconsistency uniformly across India across time scales. Even though the downscaled outputs show similar results for majority of the Indian landmass, it is highly heterogeneous across time horizons. There is also an emergence of medium evidence for future changes in ISMR for few regions of the country such as the southern Western Ghats, foothills of the Himalaya and central India. The study brings out that even‐improved simulations from downscaling fail to reliably project ISMR due to high inter‐model uncertainty and internal variability. The significant but inconsistent future changes in ISMR projected for a major portion of the Indian subcontinent, in contrast to earlier studies, pose extreme challenges to climate change impact assessments and adaptation/mitigation planning.

Hydrological response to climate change of the Brahmaputra basin using CMIP5 general circulation model ensemble

Abstract The Ganges–Brahmaputra–Meghna river system carries the world's third-largest fresh water discharge and Brahmaputra alone carries about 67% of the total annual flow of Bangladesh. Climate change will be expected to alter the hydrological cycles and the flow regime of these basins. Assessment of the fresh water availability of the Brahmaputra Basin in the future under climate change condition is crucial for both society and the ecosystem. SWAT, a semi-distributed physically based hydrological model, has been applied to investigate hydrological response of the basin. However, it is a challenging task to calibrate and validate models over this ungauged and poor data basin. A model derived by using gridded rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite and temperature data from reanalysis product ERA-Interim provides acceptable calibration and validation. Using the SWAT-CUP with SUFI-2 algorithm, sensitivity analysis of model parameters was examined. A calibrated model was derived using new climate change projection data from the multi-model ensemble CMIP5 Project over the South Asia CORDEX domain. The uncertainty of predicting monsoon flow is less than that of pre-monsoon flow. Most of the regional climate models (RCMs) show an increasing tendency of the discharge of Brahmaputra River at Bahadurabad station during monsoon, when flood usually occurs in Bangladesh.

Analysis of the variability of the North Atlantic eddy-driven jet stream in CMIP5

The North Atlantic eddy-driven jet is a dominant feature of extratropical climate and its variability is associated with the large-scale changes in the surface climate of midlatitudes. Variability of this jet is analysed in a set of General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project phase-5 (CMIP5) over the North Atlantic region. The CMIP5 simulations for the 20th century climate (Historical) are compared with the ERA40 reanalysis data. The jet latitude index, wind speed and jet persistence are analysed in order to evaluate 11 CMIP5 GCMs and to compare them with those from CMIP3 integrations. The phase of mean seasonal cycle of jet latitude and wind speed from historical runs of CMIP5 GCMs are comparable to ERA40. The wind speed mean seasonal cycle by CMIP5 GCMs is overestimated in winter months. A positive (negative) jet latitude anomaly in historical simulations relative to ERA40 is observed in summer (winter). The ensemble mean of jet latitude biases in historical simulations of CMIP3 and CMIP5 with respect to ERA40 are -2.43^circ and -1.79^circ respectively. Thus indicating improvements in CMIP5 in comparison to the CMIP3 GCMs. The comparison of historical and future simulations of CMIP5 under RCP4.5 and RCP8.5 for the period 2076–2099, shows positive anomalies in the jet latitude implying a poleward shifted jet. The results from the analysed models offer no specific improvements in simulating the trimodality of the eddy-driven jet.

Climate change and variability impacts on the forests of Bangladesh – a diagnostic discussion based on CMIP5 GCMs and ENSO

ABSTRACTThe aim of this study is to assess the evolution of forests in Bangladesh from the perspective of changing climate, with particular emphasis on the Sundarbans, the world's largest mangrove forest. Projections for temperature and rainfall change have been made using the latest Intergovernmental Panel on Climate Change‐Fifth Assessment Report (IPCC‐AR5) general circulation models (GCMs) protocol (Coupled Model Intercomparison Project Phase 5, CMIP5) with 38 various GCMs and up to 105 model runs. The GCMs findings were verified by the published results of 11 regional climate models (RCMs) in the Coordinated Regional Climate Downscaling Experiment (CORDEX)‐South Asia models with finer spatial resolution. A CMIP5 model‐based synthesis review on the future variability of El Niño‐Southern Oscillation (ENSO) has also been made, as both the seasonal temperature and rainfall variability in Bangladesh are correlated to ENSO. Both global and regional climate change scenarios indicate that temperature would continue to rise and total annual rainfall is likely to increase in the future. However, weak increases in rainfall combined with high increases in temperature would create a high demand and competition for water because of the higher evapotranspiration environment. In addition to changing climate, the interannual variability of ENSO would further aggravate problems because it displays important correlations with the variability of rainfall, temperatures, sea‐level, lightning, and forest fires in Bangladesh. This study strongly recommends that future climate change projections should be combined with interannual variability ENSO to improve climate perspectives for stronger forest capacity building efforts in Bangladesh.

Land–Ocean Shifts in Tropical Precipitation Linked to Surface Temperature and Humidity Change

A compositing scheme that predicts changes in tropical precipitation under climate change from changes in near-surface relative humidity (RH) and temperature is presented. As shown by earlier work, regions of high tropical precipitation in general circulation models (GCMs) are associated with high near-surface RH and temperature. Under climate change, it is found that high precipitation continues to be associated with the highest surface RH and temperatures in most CMIP5 GCMs, meaning that it is the “rank” of a given GCM grid box with respect to others that determines how much precipitation falls rather than the absolute value of surface temperature or RH change, consistent with the weak temperature gradient approximation. Further, it is demonstrated that the majority of CMIP5 GCMs are close to a threshold near which reductions in land RH produce large reductions in the RH ranking of some land regions, causing reductions in precipitation over land, particularly South America, and compensating increases over ocean. Recent work on predicting future changes in specific humidity allows the prediction of the qualitative sense of precipitation change in some GCMs when land surface humidity changes are unknown. However, the magnitudes of predicted changes are too small. Further study, perhaps into the role of radiative and land–atmosphere feedbacks, is necessary.