Generation Coupled Global Climate Model Research Articles

Simulating Soil Organic Carbon Responses to Cropping Intensity, Tillage, and Climate Change in Pacific Northwest Dryland.

Managing dryland cropping systems to increase soil organic C (SOC) under changing climate is challenging after decades of winter wheat ( L.)-fallow and moldboard plow tillage (W-F/MP). The objective was to use CQESTR, a process-based C model, and SOC data collected in 2004, 2008, and 2012 to predict the best management to increase SOC under changing climate in four cropping systems, which included continuous wheat under no tillage (W-W/NT), wheat and sorghum × sudangrass [ (L.) Moench. × L.] under no tillage, wheat-fallow under sweep tillage, and W-F/MP. Since future yields and climate are uncertain, 20 scenarios for each cropping system were simulated with four climate projections and five crop yield scenarios (current crop yields, and 10 or 30% greater or lesser yields). Measured and simulated SOC were significantly ( < 0.0001) correlated ( = 0.98) at all soil depths. Predicted SOC changes ranged from -12.03 to 2.56 Mg C ha in the 1-m soil depth for W-F/MP and W-W/NT, respectively, during the 2012 to 2052 predictive period. Only W-W/NT sequestered SOC at a rate of 0.06 Mg C ha yr under current crop yields and climate. Under climate change and yield scenarios, W-W/NT lost SOC except with a 30% wheat yield increase for 40 yr. Predicted SOC increases in W-W/NT were 0.71, 1.16, and 0.88 Mg C ha under the Oregon Climate Assessment Reports for low emissions and high emissions and the Regional Climate Model version 3 with boundary conditions from the Third Generation Coupled Global Climate Model, respectively, with 30% yield increases. Continuous no-till cropping would increase SOC and improve soil health and resiliency to lessen the impact of extreme weather.

Impact of climate change on financial analysis of a small hydropower project

Wise prediction of discharge is vital for effective utilization of water resources and hydropower generation. There is no doubt that the future climate change will substantially affect precipitation amount, discharge and hydro-meteorology which are considered as major sources for hydropower energy. In this study, we present an application of downscaling technique on prediction of discharge of a local gage station in Turkey. Variable input set of the Third Generation Coupled Global Climate Model variables are statistically downscaled and coupled with variable forms of local discharge (Q, LnQ, MavQ, StdQ, and Q/Qmax) by using five different models as Gene-expression programming, Group Method of Data Handling, K-nearest neighbour, logistic regression and linear regression. Different sub-models are developed and calibrated based on the training period, and the best model is selected based on the testing period. Future projecting of discharge is done based on the Third Generation Coupled Global Climate Model A2 scenario, and the financial analysis is carried out by using this forecasted Q values.

A Novel Spatiotemporal Statistical Downscaling Method for Hourly Rainfall

Finer spatiotemporal resolution rainfall data is essential for assessing hydrological impacts of climate change on medium and small basins. However, existing methods pay less attention to the inter-day correlation and diurnal cycle, which can strongly influence the hydrological cycle. To address this problem, we present a spatiotemporal downscaling method that is capable of reproducing the inter-day correlation, the diurnal cycle, and rainfall statistics on daily and hourly scales. The large-scale datasets, which we obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis dataset (NNR) and general circulation model (GCM) outputs, and local rainfall data are analyzed to assess the impacts of climate change on rainfall. Our proposed method consists of two steps: spatial downscaling and temporal downscaling. We apply spatial downscaling first to obtain the relationship between large-scale datasets and daily rainfall at a site scale using a k-nearest neighbor method (KNN). Then, we conduct an hourly downscaling of daily rainfall in the second step using a genetic algorithm-based KNN (GAKNN) with the inter-day correlation and the diurnal cycle. Furthermore, we analyzed changes in rainfall statistics for the periods 2046–2065 and 2081–2100 under the A2, A1B, and B1 scenarios of the third generation Coupled Global Climate Model (CGCM3.1) and Bergen Climate Model version 2 (BCM2.0). An application of our proposed method to the Shihmen Reservoir basin (Taiwan) has shown that it could accurately reproduce local rainfall and its statistics on daily and hourly scales. Overall, the results demonstrated that the proposed spatiotemporal method is a powerful tool for downscaling hourly rainfall data from a large-scale dataset. The understanding of future changes of rainfall characteristics through our proposed method is also expected to assist the planning and management of water resources systems.

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.

Reservoir management under predictable climate variability and change

The potential effects of climate change on future water budget components and streamflow in the Mississippi River (Ontario) are assessed. Analyses of historic hydrometric data indicate an increasing trend in winter streamflows due to the rising winter air temperatures across the region over the latter half of the 20th century. These temperatures have resulted in reduced snow accumulation and earlier spring snowmelt. Projected future climate data are developed using the second generation Coupled Global Climate Model and downscaled using the change factor method for the Mississippi River watershed (Ontario). The projected future climate data are then used as input to a calibrated hydrologic model for simulation of future water balance and streamflows in this river basin. These simulations predict a gradual annual rate of change of: 0.1% increase in total precipitation; 0.2% increase in rainfall; 0.7% decrease in snowfall; 0.2% increase in potential evapotranspiration; 0.1% decrease in soil moisture; 1.4% increase in water deficit; 0.5% increase in streamflow during winter months; and 0.3% decrease in summer streamflows. Cyclic pattern analysis of the historic streamflow records suggests the existence of pronounced 3-year and 12-year cycles, providing short-term streamflow forecasting opportunities for optimum reservoir management operations during the wet-year/dry-year cycles.

Effects of climate change on daily minimum and maximum temperatures and cloudiness in the Shikoku region: a statistical downscaling model approach

In this study, we present a detailed analysis of the effect of changes in cloudiness (CLD) between a future period (2071–2099) and the base period (1961–1990) on daily minimum temperature (TMIN) and maximum temperature (TMAX) in the same period for the Shikoku region, Japan. This analysis was performed using climate data obtained with the use of the Statistical DownScaling Model (SDSM). We calibrated the SDSM using the National Center for Environmental Prediction (NCEP) reanalysis dataset for the SDSM input and daily time series of temperature and CLD from 10 surface data points (SDP) in Shikoku. Subsequently, we validated the SDSM outputs, specifically, TMIN, TMAX, and CLD, obtained with the use of the NCEP reanalysis dataset and general circulation model (GCM) data against the SDP. The GCM data used in the validation procedure were those from the Hadley Centre Coupled Model, version 3 (HadCM3) for the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios and from the third generation Coupled Global Climate Model (CGCM3) for the SRES A2 and A1B scenarios. Finally, the validated SDSM was run to study the effect of future changes in CLD on TMIN and TMAX. Our analysis showed that (1) the negative linear fit between changes in TMAX and those in CLD was statistically significant in winter while the relationship between the two changes was not evident in summer, (2) the dependency of future changes in TMAX and TMIN on future changes in CLD were more evident in winter than in other seasons with the present SDSM, (3) the diurnal temperature range (DTR) decreased in the southern part of Shikoku in summer in all the SDSM projections while DTR increased in the northern part of Shikoku in the same season in these projections, (4) the dependencies of changes in DTR on changes in CLD were unclear in summer and winter. Results of the SDSM simulations performed for climate change scenarios such as those from this study contribute to local-scale agricultural and hydrological simulations and development of agricultural and hydrological models.

Modelling potential impacts of climate change on water and nitrate export from a mid-sized, semiarid watershed in the US Southwest

The impacts of climate change on water and nitrogen cycles in arid central Arizona (USA) were investigated by integrating the Second Generation Coupled Global Climate Model (CGCM2) and a widely used, physical process-based model, Soil and Water Assessment Tool (SWAT). With statistically downscaled daily climate data from the CGCM2 as model input, SWAT predicted increased potential evapotranspiration and decreased surface runoff, lateral flow, soil water, and groundwater recharge, which suggests serious consequences for the water cycle in this desert catchment in the future. Specifically, stream discharge is projected to decrease by 31 % in the 2020s, 47 % in the 2050s, and 56 % in the 2080s compared to the mean discharge for the base period (0.73 m(3)/s). A flow-duration analysis reveals that the projected reduction of stream discharge in the future is attributable to significant decreases in mid-range and low-flow conditions; however, flood peaks would show a slight increase in the future. The drier and hotter future also will decrease the rate of nitrogen mineralization in the catchment and ultimately, nitrate export from the stream. Since mean mineralization rate would decrease by 15 % in the 2020s, 28 % in the 2050s, and 35 % in the 2080s compared to the based period (9.3 g N ha(-1) d(-1)), the combined impact of reduced catchment mineralization and reduced streamflow would predict declining nitrate export: from today's mean value of 30 kg N/d, to 20, 15 and 12 kg N/d by the 2020s, 2050s, and 2080s, respectively.

Dynamical Downscaling over the Gulf of St. Lawrence using the Canadian Regional Climate Model

This study explores the problem of dynamical downscaling global climate model (GCM) simulations to finer resolution and compares the results with present climate reanalysis data. We use the Canadian Regional Climate Model (CRCM) to dynamically downscale outputs from the third generation Coupled Global Climate Model (CGCM3) for the Gulf of St. Lawrence and related coastal areas. The integration was performed for the 1970–99 tri-decadal period. Three methodologies were used: (DM-1) sea surface temperature (SST) fields and other fields from CGCM3 were used as drivers of the CRCM, (DM-2) CGCM3 SSTs were adjusted based on North American Regional Reanalysis (NARR) data to correct SST climate biases over the ocean, and (DM-3) CGCM3 SSTs were adjusted based on NARR data (as in (DM-2)) and longwave radiation was adjusted to remove biases in the CRCM's land surface temperature. The DM-1 methodology, using SSTs from CGCM3, gives surface temperature estimates that are too cold in summer and too warm in winter and underestimates 10 m winds in the summer. By comparison, the DM-2 and DM-3 methodologies produce more accurate estimates of marine winds and surface air temperature compared to the CGCM3 results, particularly in coastal areas. Differences among these methodologies are relatively minor at upper levels of the atmosphere. RÉSUMÉ [Traduit par la rédaction] Cette étude explore le problème de la réduction d’échelle dynamique des simulations du modèle climatique du globe (GCM) à de plus fines résolutions et compare les résultats aux données réanalysées du climat présent. Nous utilisons le modèle régional canadien du climat (CRCM) pour réduire dynamiquement l’échelle des sorties du modèle couplé climatique du globe (CGCM3) pour le golfe du Saint-Laurent et les régions côtières avoisinantes. Nous avons effectué l'intégration pour les trois décennies de la période 1970–1999. Nous avons utilisé trois méthodologies : (DM-1) nous avons utilisé les champs de température de la surface de la mer et d'autres champs du CGCM3 pour piloter le CRCM; (DM-2) nous avons ajusté les températures de surface de la mer du CGCM3 en fonction des données NARR (North American Regional Reanalysis) pour corriger les biais climatiques au-dessus de l'océan; et (DM-3) nous avons ajusté les températures de surface de la mer du CGCM3 en fonction des données NARR — comme dans (DM-2) — et nous avons ajusté le rayonnement de grandes longueurs d'onde pour enlever les biais dans les températures de surface de la terre du CRCM. La méthodologie DM-1, qui utilise les températures de surface de la mer du CGCM3, donne des estimations de température de surface qui sont trop basses en été et trop élevées en hiver et sous-estime le vent à 10 m en été. Par contre, les méthodologies DM-2 et DM-3 produisent des estimations plus précises des vents marins et des températures de l'air en surface par comparaison aux résultats du CGCM3, en particulier dans les régions côtières. Les différences entre ces méthodologies sont relativement faibles dans les niveaux supérieurs de l'atmosphère.

Evaluating the effects of future climate change and elevated CO 2 on the water use efficiency in terrestrial ecosystems of China

Water use efficiency (WUE) is an important variable used in climate change and hydrological studies in relation to how it links ecosystem carbon cycles and hydrological cycles together. However, obtaining reliable WUE results based on site-level flux data remains a great challenge when scaling up to larger regional zones. Biophysical, process-based ecosystem models are powerful tools to study WUE at large spatial and temporal scales. The Integrated BIosphere Simulator (IBIS) was used to evaluate the effects of climate change and elevated CO 2 concentrations on ecosystem-level WUE (defined as the ratio of gross primary production (GPP) to evapotranspiration (ET)) in relation to terrestrial ecosystems in China for 2009–2099. Climate scenario data (IPCC SRES A2 and SRES B1) generated from the Third Generation Coupled Global Climate Model (CGCM3) was used in the simulations. Seven simulations were implemented according to the assemblage of different elevated CO 2 concentrations scenarios and different climate change scenarios. Analysis suggests that (1) further elevated CO 2 concentrations will significantly enhance the WUE over China by the end of the twenty-first century, especially in forest areas; (2) effects of climate change on WUE will vary for different geographical regions in China with negative effects occurring primarily in southern regions and positive effects occurring primarily in high latitude and altitude regions (Tibetan Plateau); (3) WUE will maintain the current levels for 2009–2099 under the constant climate scenario (i.e. using mean climate condition of 1951–2006 and CO 2 concentrations of the 2008 level); and (4) WUE will decrease with the increase of water resource restriction (expressed as evaporation ratio) among different ecosystems.

Scenario changes in the climatology of winter midlatitude cyclone activity over eastern North America and the Northwest Atlantic

The present study explores how midlatitude winter cyclone activity can be modified under warming‐induced conditions due to enhanced greenhouse gas concentrations. We performed simulations with the Canadian Regional Climate Model (CRCM version 3.5) implemented on a domain that covers the Northwest Atlantic and eastern North America. These simulations are driven by control conditions (1975–1994) and high‐CO2 scenario conditions (2040–2059) suggested by the Canadian Climate Centre model, CGCM2 (Second Generation Coupled Global Climate Model), following the IPCC IS92a scenario. Comparisons between model simulations for the control period (1975–1994) and North America Regional analysis (NARR) suggest that both CGCM2 and CRCM reliably reproduce the overall NARR patterns of sea level pressure, tropospheric baroclinicity and Atlantic storm tracks. However, compared to CGCM2 results, CRCM offers an improvement in simulations of the most intense cyclones. Although both models underestimate the track density of intense cyclones, the CGCM2 underestimates are larger than those of CRCM. Under the high‐CO2 climate change scenario, the CRCM and CGCM2 model simulations show similar changes in sea level pressure, surface temperature, and total track density of midlatitude winter cyclones. Although we can see the northwest shift of the dominant Atlantic storm track, it is not statistically significant. Moreover, simulations from both models show a decrease in the total cyclone track density along the Canadian east coast; the decrease is more robust in CRCM simulations than in CGCM2 results. For intense cyclones, CRCM simulations show a slight decrease in the track density, while no such change is found in CGCM2 simulations.

Characteristics of atmosphere‐ocean interactions along North Atlantic extratropical storm tracks

This study explores the characteristics of the air‐sea interactions and extratropical autumn storms in the northwest Atlantic. Simulations are performed with a relatively fine resolution coupled atmosphere‐ocean model system. The model system consists of the Canadian Mesoscale Compressible Community atmospheric model coupled to a recent version of the Princeton Ocean model. Atmospheric boundary conditions for storm simulations are given by the Canadian Climate Centre model, Second Generation Coupled Global Climate Model (CGCM2), following the Intergovernmental Panel on Climate Change IS92a scenario conditions. The CGCM2 control conditions are obtained from simulated years 1975–1994. Our results show that interactions between extratropical storms and the upper ocean are affected by the upper ocean, latent and sensible heat fluxes across the air‐sea interface, and the atmospheric structure, which propagates relative to the ocean surface as storms move along their storm tracks. Three types of numerical storm experiments are conducted, for storms that (1) make landfall, (2) move along the coastline without landfall, and (3) remain over the open ocean. Our results suggest that the locations of the maximum storm‐induced sea surface temperature (SST) cooling are mainly dominated by the upper ocean thermostructure. The storm's intensity, as represented by its maximum wind speed, can cause further SST cooling. In turn, the upper ocean can reduce storm intensity by as much as 4–5 hPa in minimum sea level pressure, or 2–4 m s−1 in surface winds. Although the impacts of these effects are most notable in the lower atmospheric boundary layer, they extend to the top of the troposphere.

Evaluation of uncertainties in the CRCM-simulated North American climate

This work is a first step in the analysis of uncertainty sources in the RCM-simulated climate over North America. Three main sets of sensitivity studies were carried out: the first estimates the magnitude of internal variability, which is needed to evaluate the significance of changes in the simulated climate induced by any model modification. The second is devoted to the role of CRCM configuration as a source of uncertainty, in particular the sensitivity to nesting technique, domain size, and driving reanalysis. The third study aims to assess the relative importance of the previously estimated sensitivities by performing two additional sensitivity experiments: one, in which the reanalysis driving data is replaced by data generated by the second generation Coupled Global Climate Model (CGCM2), and another, in which a different CRCM version is used. Results show that the internal variability, triggered by differences in initial conditions, is much smaller than the sensitivity to any other source. Results also show that levels of uncertainty originating from liberty of choices in the definition of configuration parameters are comparable among themselves and are smaller than those due to the choice of CGCM or CRCM version used. These results suggest that uncertainty originated by the CRCM configuration latitude (freedom of choice among domain sizes, nesting techniques and reanalysis dataset), although important, does not seem to be a major obstacle to climate downscaling. Finally, with the aim of evaluating the combined effect of the different uncertainties, the ensemble spread is estimated for a subset of the analysed simulations. Results show that downscaled surface temperature is in general more uncertain in the northern regions, while precipitation is more uncertain in the central and eastern US.

The Impacts of Climate Change on Autumn North Atlantic Midlatitude Cyclones

Abstract This study explores how midlatitude extratropical cyclone intensities, frequencies, and tracks can be modified under warming-induced conditions due to enhanced greenhouse gas (GHG) concentrations. Simulations were performed with the Canadian mesoscale compressible community (MC2) model driven by control and high CO2 climate estimates from the Canadian Climate Centre model, the Second Generation Coupled Global Climate Model (CGCM2). CGCM2 simulations have effective CO2 concentration forcing, following the Intergovernmental Panel on Climate Change (IPCC) IS92a scenario conditions, which define a near doubling of CO2 concentrations by 2050 compared to the 1980s. The control and high CO2 conditions were obtained from years 1975–94 and 2040–59 of CGCM2 simulations. For the northwest Atlantic, the CO2-induced warming for this period (2040–59) varies from ∼1°–2°C in the subtropics, near the main development region for Atlantic hurricanes, to ∼1°C in the north. In simulations of northwest Atlantic storms, the net impact of this enhanced CO2 scenario is to cause storms to increase in radius, with marginal tendencies to become more severe and to propagate faster (although not statistically significant), and for the mean storm tracks to shift slightly poleward.

Downscaling of precipitation for climate change scenarios: A support vector machine approach

The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.