General Circulation Model Climate Research Articles

Increased burning in a warming climate reduces carbon uptake in the Greater Yellowstone Ecosystem despite productivity gains

Abstract The effects of changing climate and disturbance on mountain forest carbon (C) stocks vary with tree species distributions and over elevational gradients. Warming can not only increase C uptake by stimulating productivity at high elevations but also enhance C release by increasing respiration and the frequency, intensity and size of wildfires. To understand the consequences of climate change for temperate mountain forests, we simulated interactions among climate, wildfire, tree species and their combined effects on regional C stocks in forests of the Greater Yellowstone Ecosystem, USA (GYE) with the LANDIS‐II landscape change model. Simulations used historical climate and future potential climate represented by downscaled projections from five general circulation models (GCMs) that bracket the range of variability under the representative concentration pathway (RCP) 8.5 emissions scenario. Total ecosystem C increased by 67% through 2100 in simulations with historical climate, and by 38%–69% with GCM climate. Differences in C uptake among GCMs resulted primarily from variation in area burned, not productivity. Warming increased productivity by extending the growing season, especially near upper tree line, but did not offset biomass losses to fire. By 2100, simulated area burned increased by 27%–215% under GCM climate, with the largest increases after 2050. With warming >3°C in mean annual temperature, the increased frequency of large fires reduced live C stocks by 4%–36% relative to the control, historical climate scenario. However, relative losses in total C were delayed under GCMs with large increases in summer precipitation and buffered by C retained in soils and the wood of fire‐killed trees. Increasing fire size limited seed dispersal, and reductions in soil moisture limited seedling establishment; both effects will likely constrain long‐term forest regeneration and C uptake. Synthesis. Forests in the GYE can maintain a C sink through the mid‐century in a warming climate but continued warming may cause the loss of forest area, live above‐ground biomass and, ultimately, ecosystem C. Future changes in C stocks in similar forests throughout western North America will depend on regional thresholds for extensive wildfire and forest regeneration and therefore, changes may occur earlier in drier regions.

Assessment of hydrological response in Subarnarekha river basin under anticipated climate change scenarios

<p>Two hydrological climate modelling techniques, general circulation model (GCM) and hypothetical climate change scenarios, were used to analyse the hydrological response to the anticipated climate change scenarios in the Subarnarekha river basin in Eastern India. Both models verified individually for the same river basin and a comparative performance of the models was evaluated to relate the two models for the near (2014-2040) period climate. The hydrological response under the anticipated climate change in the Subarnarekha river basin is well assessed by GCM under the RCP 8.5 scenarios compared to the RCPs 4.5. Results indicate GCM best suited over the hypothetical climate change scenarios as GCM has demonstrated their potential in accurately reproducing the past observed climatic changes. The strong performance of the hypothetical climate change scenarios model, particularly for warming climate scenarios, suggests that it may have distinct advantages for the analysis of water balance components in the river basin. The monthly streamflows of Subarnarekha river basin was simulated using a total of 14 years (2000-2013) daily observed streamflow data in the ArcSWAT model integrated with model calibration and uncertainty analysis by means of SUFI-2 algorithm. The results indicate during the calibration the coefficient of determination (R2) and Nash-Sutcliff Efficiency (NSE) were reported as 0.98 and 0.97, respectively, while during the validation the R2 and NSE were obtained as 0.94 and 0.94, respectively, confirms the hydrological model performance was very good both in calibration and validation. The obtained climate change water impact index (ICCWI) values reveal the Subarnarekha river basin is more responsive to climate change. The reduction in precipitation along with the significant warming under the projected future climate is likely to reduce availability of water substantially in the study region. This work would be useful for the effective management of water resources for sustainable agriculture and in mitigating natural hazards such as droughts and floods in the study region.</p>

Predicting sediment discharges and erosion rates in deep time—examples from the late Cretaceous North American continent

AbstractDepositional stratigraphy represents the only physical archive of palaeo‐sediment routing and this limits analysis of ancient source‐to‐sink systems in both space and time. Here, we use palaeo‐digital elevation models (palaeoDEMs; based on high‐resolution palaeogeographic reconstructions), HadCM3L general circulation model climate data and the BQART suspended sediment discharge model to demonstrate a predictive, forward approach to palaeo‐sediment routing system analysis. To exemplify our approach, we use palaeoDEMs and HadCM3L data to predict the configurations, geometries and climates of large continental catchments in the Cenomanian and Turonian North American continent. Then, we use BQART to estimate suspended sediment discharges and catchment‐averaged erosion rates and we map their spatial distributions. We validate our estimates with published geologic constraints from the Cenomanian Dunvegan Formation, Alberta, Canada, and the Turonian Ferron Sandstone, Utah, USA, and find that estimates are consistent or within a factor of two to three. We then evaluate the univariate and multivariate sensitivity of our estimates to a range of uncertainty margins on palaeogeographic and palaeoclimatic boundary conditions; large uncertainty margins (≤50%/±5°C) still recover estimates of suspended sediment discharge within an order of magnitude of published constraints. PalaeoDEMs are therefore suitable as a first‐order investigative tool in palaeo‐sediment routing system analysis and are particularly useful where stratigraphic records are incomplete. We highlight the potential of this approach to predict the global spatio‐temporal response of suspended sediment discharges and catchment‐averaged erosion rates to long‐period tectonic and climatic forcing in the geologic past.

Propagation of Error and the Reliability of Global Air Temperature Projections

The reliability of general circulation climate model (GCM) global air temperature projections is evaluated for the first time, by way of propagation of model calibration error. An extensive series of demonstrations show that GCM air temperature projections are just linear extrapolations of fractional greenhouse gas (GHG) forcing. Linear projections are subject to linear propagation of error. A directly relevant GCM calibration metric is the annual average 12.1% error in long-wave cloud forcing (LWCF) produced within CMIP5 climate models. This error is strongly pair-wise correlated across models, implying a source in deficient theory. Climate model LWCF error introduces an annual average ±4 Wm-2 uncertainty into the simulated tropospheric thermal energy flux. This annual ±4 Wm-2 simulation uncertainty is 114 larger than the annual average ~0.035 Wm-2 change in tropospheric thermal energy flux produced by increasing GHG forcing since 1979. Tropospheric thermal energy flux is the determinant of global air temperature. Uncertainty in simulated tropospheric thermal energy flux imposes uncertainty on projected air temperature. Propagation of LWCF thermal energy flux error through the historically relevant 1988 projections of GISS Model II scenarios A, B, and C, the IPCC SRES scenarios CCC, B1, A1B, and A2, and the RCP scenarios of the 2013 IPCC Fifth Assessment Report, uncovers a ±15 C uncertainty in air temperature at the end of a centennial-scale projection. Analogously large but previously unrecognized uncertainties must therefore exist in all the past and present air temperature projections and hindcasts of even advanced climate models. The unavoidable conclusion is that an anthropogenic air temperature signal cannot have been, nor presently can be, evidenced in climate observables.

Climatic effects on ice phenology and ice-jam flooding of the Athabasca River in western Canada

ABSTRACTIn cold region environments, any alteration in the hydro-climatic regime can have profound impacts on river ice processes. This paper studies the implications of hydro-climatic trends on river ice processes, particularly on the freeze-up and ice-cover breakup along the Athabasca River in Fort McMurray in western Canada, which is an area very prone to ice-jam flooding. Using a stochastic approach in a one-dimensional hydrodynamic river ice model, a relationship between overbank flow and breakup discharge is established. Furthermore, the likelihood of ice-jam flooding in the future (2041–2070 period) is assessed by forcing a hydrological model with meteorological inputs from the Canadian regional climate model driven by two atmospheric–ocean general circulation climate models. Our results show that the probability of ice-jam flooding for the town of Fort McMurray in the future will be lower, but extreme ice-jam flood events are still probable.

Simulating the effects of climate variability on waterbodies and wetland‐dependent birds in the Prairie Pothole Region

AbstractUnderstanding how bird populations respond to changes in waterbody availability in the climatically variable Prairie Pothole Region (PPR) of North America hinges on being able to couple hydrological and climate modeling to represent potential future landscapes. Model experiments run with the Pothole Complex Hydrologic Model using downscaled climate data (variables relating to precipitation, temperature, and potential evapotranspiration at 1/8° spatial resolution under four general circulation climate models and two gas emissions scenarios) were used to forecast the abundances of six focal wetland‐dependent bird species in the Missouri Coteau portion of the PPR, providing ensemble scenarios at a spatial scale relevant to resource management. Although the projected number of May ponds (waterbodies present during bird breeding season) fluctuated through time with some decadal periodicity (and with the number present in a given year reflecting abundance over the previous three years), the ensemble model average indicated an increase in the average number of waterbodies present by the turn of the next century. Overall, the model experiments conservatively projected an 11.75% increase in the number of waterbodies present by 2090–2099 compared to a baseline period from 1967 to 2005 in the PPR. Wetland‐dependent bird occurrence and abundance were significantly associated with temporal patterns and decadal periodicity in waterbody dynamics. Because of the strong associations between wetland‐dependent bird occurrence and abundance and the number of prairie potholes, projected waterbody increases are forecasted to result in an 11.97% overall increase in occurrence and 8.63% increase in abundance of the six focal species by the end of the 21st century; these results contrast with forecasted drought‐associated declines in waterbodies and birds in the PPR. This integrated hydrological–climatological approach offers a means of assessing how wetland‐dependent bird populations may respond to changes in wetland habitat availability due to a changing climate. Our results provide information that can help managers decide how to mitigate the effects of climate shifts on the distribution of wetland habitat and biota.

Comparison of multiple downscaling techniques for climate change projections given the different climatic zones in China

General circulation models (GCMs) are important tools for the study of climate change, but their resolutions are too coarse for station-scale impact assessments. Statistical and dynamical downscaling methods are widely used to translate the predictions of GCMs to the finer spatial scale and it is important to understand the difference between statistical and dynamical downscaling methods in different climatic zones and time periods. Moreover, statistical downscaling can be used on both GCM and regional climate model (RCM) outputs. In this study, two sets of GCM precipitations were dynamically and statistically downscaled and their performances were evaluated against the observed precipitation from 308 stations distributed throughout the Yellow, Yangtze, and Pearl River basins. These stations have distinct climatic characteristics from the historical period (1961–2000) and future period (2031–2050). Results suggest dynamically downscaled GCM precipitation does not present lower biases when comparing observed site-specific precipitation to GCM outputs, and biases of the initial dynamically downscaled GCM outputs decreased in areas with higher humidity. This demonstrates that statistical downscaling can improve GCM and RCM outputs, and the statistical downscaling method can reproduce local-scale precipitation satisfactorily without dynamical downscaling. However, statistical downscaling reduced spatial regularity of the biases that exist in GCM and RCM outputs between the observations and simulation. Additionally, the spatial discrepancy between statistically downscaled GCM and RCM precipitations was very small. In the future period, discrepancies between statistically downscaled RCM and GCM precipitations in the two climate scenarios were larger than the historical period for all climate zones.

Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions

Freshwater species are adapted to and depend on various discharge conditions, such as 32 indicators of hydrologic alteration (IHA). Knowing how these indicators will be altered under climate change is essential for predicting species response and to develop mitigation concepts. The simulation of IHA under climate change is subject to considerable uncertainties which should be considered to obtain credible and robust predictions. Therefore, we investigated the major uncertainties inherent in climate change data and processing: general circulation model (GCM) and regional climate model (RCM) choice, representative concentration pathway (RCP) scenario, bias correction (BC) method, all within three mesoscale catchments in the European ecoregions: Central Plains, Central Highlands, and Alpine. Highest uncertainties were caused by the GCM and RCM choice, followed by the type of BC and the RCP. For the prediction, we reduced these uncertainties tailored to the ideal depiction of the IHA in each ecoregion. Together with a significance test, this enabled a robust depiction of the change in IHA for two future time periods. We found diverging changes within the ecoregions, caused by the complex interaction between precipitation, temperature and the governing catchment hydrological processes. The results provide an important basis for further impact research, especially for ecological freshwater studies.

Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)

This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for the step-wise shortlisting and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on: (a) range of projected mean changes, (b) range of projected extreme changes, and (c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, from of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to the year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range from a slight annual increase of 2.2% under the drier scenarios to as high as 15.9% in the wet scenarios. Moreover, for all scenarios, future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century.

Signature of present and projected climate change at an urban scale: The case of Addis Ababa

Understanding climate change and variability at an urban scale is essential for water resource management, land use planning, development of adaption plans, mitigation of air and water pollution. However, there are serious challenges to meet these goals due to unavailability of observed and/or simulated high resolution spatial and temporal climate data. The statistical downscaling of general circulation climate model, for instance, is usually driven by sparse observational data hindering the use of downscaled data to investigate urban scale climate variability and change in the past. Recently, these challenges are partly resolved by concerted international effort to produce global and high spatial resolution climate data. In this study, the 1 km2 high resolution NIMR-HadGEM2-AO simulations for future projections under Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios and gridded observations provided by Worldclim data center are used to assess changes in rainfall, minimum and maximum temperature expected under the two scenarios over Addis Ababa city. The gridded 1 km2 observational data set for the base period (1950–2000) is compared to observation from a meteorological station in the city in order to assess its quality for use as a reference (baseline) data. The comparison revealed that the data set has a very good quality. The rainfall anomalies under RCPs scenarios are wet in the 2030s (2020–2039), 2050s (2040–2069) and 2080s (2070–2099). Both minimum and maximum temperature anomalies under RCPs are successively getting warmer during these periods. Thus, the projected changes under RCPs scenarios show a general increase in rainfall and temperatures with strong variabilities in rainfall during rainy season implying level of difficulty in water resource use and management as well as land use planning and management.

GCM Simulations of Unstable Climates in the Habitable Zone

Abstract It has recently been proposed that Earth-like planets in the outer regions of the habitable zone experience unstable climates, repeatedly cycling between glaciated and deglaciated climatic states. While this result has been confirmed and also extended to explain early Mars climate records, all existing work relies on highly idealized low-dimensional climate models. Here, we confirm that the phenomenology of climate cycles remains in 3D Earth climate models with considerably more degrees of freedom. To circumvent the computational barrier of integrating climate on Gyr timescales, we implement a hybrid 0D-3D integrator that uses a general circulation model (GCM) as a short relaxation step along a long evolutionary climate sequence. We find that GCM climate cycles are qualitatively consistent with reported low-dimensional results. This establishes on a firmer ground the notion that outer habitable zone planets may be preferentially found in transiently glaciated states.

High‐resolution projections of 21st century climate over the Athabasca River Basin through an integrated evaluation‐classification‐downscaling‐based climate projection framework

AbstractAn evaluation‐classification‐downscaling‐based climate projection (ECDoCP) framework is developed to fill a methodological gap of general circulation models (GCMs)‐driven statistical‐downscaling‐based climate projections. ECDoCP includes four interconnected modules: GCM evaluation, climate classification, statistical downscaling, and climate projection. Monthly averages of daily minimum (Tmin) and maximum (Tmax) temperature and daily cumulative precipitation (Prec) over the Athabasca River Basin (ARB) at a 10 km resolution in the 21st century under four Representative Concentration Pathways (RCPs) are projected through ECDoCP. At the octodecadal scale, temperature and precipitation would increase; after bias correction, temperature would increase with a decreased increment, while precipitation would increase only under RCP 8.5. Interannual variability of climate anomalies would increase from RCPs 4.5, 2.6, 6.0 to 8.5 for temperature and from RCPs 2.6, 4.5, 6.0 to 8.5 for precipitation. Bidecadal averaged climate anomalies would decrease from December‐January‐February (DJF), March‐April‐May (MAM), September‐October‐November (SON) to June‐July‐August (JJA) for Tmin, from DJF, SON, MAM to JJA for Tmax, and from JJA, MAM, SON to DJF for Prec. Climate projection uncertainties would decrease in May to September for temperature and in November to April for precipitation. Spatial climatic variability would not obviously change with RCPs; climatic anomalies are highly correlated with climate‐variable magnitudes. Climate anomalies would decrease from upstream to downstream for temperature, and precipitation would follow an opposite pattern. The north end and the other zones would have colder and warmer days, respectively; precipitation would decrease in the upstream and increase in the remaining region. Climate changes might lead to issues, e.g., accelerated glacier/snow melting, deserving attentions of researchers and the public.

Spread in the magnitude of climate model interdecadal global temperature variability traced to disagreements over high-latitude oceans.

Unforced variability in global mean surface air temperature can obscure or exaggerate global warming on interdecadal timescales, thus understanding both the magnitude and generating mechanisms of such variability is of critical importance for both attribution studies as well as decadal climate prediction. Coupled atmosphere-ocean general circulation models (climate models) simulate a wide range of magnitudes of unforced interdecadal variability in global mean surface air temperature (UITglobal), hampering efforts to quantify the influence of UITglobal on contemporary global temperature trends. Recently, a preliminary consensus has emerged that unforced interdecadal variability in local surface temperatures (UITlocal) over the tropical Pacific Ocean are particularly influential on UITglobal. Therefore, a reasonable hypothesis might be that the large spread in the magnitude of UITglobal across climate models can be explained by the spread in the magnitude of simulated tropical Pacific UITlocal. Here we show that this hypothesis is mostly false. Instead, the spread in the magnitude of UITglobal is linked much more strongly to the spread in the magnitude of UITlocal over high-latitude regions characterized by significant variability in oceanic convection, sea ice concentration, and energy flux at both the surface and the top of the atmosphere (TOA). Thus, efforts to constrain the climate model produced range of UITglobal magnitude would be best served by focusing on the simulation of air-sea interaction at high latitudes.

Understanding the past to interpret the future: comparison of simulated groundwater recharge in the upper Colorado River basin (USA) using observed and general-circulation-model historical climate data

In evaluating potential impacts of climate change on water resources, water managers seek to understand how future conditions may differ from the recent past. Studies of climate impacts on groundwater recharge often compare simulated recharge from future and historical time periods on an average monthly or overall average annual basis, or compare average recharge from future decades to that from a single recent decade. Baseline historical recharge estimates, which are compared with future conditions, are often from simulations using observed historical climate data. Comparison of average monthly results, average annual results, or even averaging over selected historical decades, may mask the true variability in historical results and lead to misinterpretation of future conditions. Comparison of future recharge results simulated using general circulation model (GCM) climate data to recharge results simulated using actual historical climate data may also result in an incomplete understanding of the likelihood of future changes. In this study, groundwater recharge is estimated in the upper Colorado River basin, USA, using a distributed-parameter soil-water balance groundwater recharge model for the period 1951–2010. Recharge simulations are performed using precipitation, maximum temperature, and minimum temperature data from observed climate data and from 97 CMIP5 (Coupled Model Intercomparison Project, phase 5) projections. Results indicate that average monthly and average annual simulated recharge are similar using observed and GCM climate data. However, 10-year moving-average recharge results show substantial differences between observed and simulated climate data, particularly during period 1970–2000, with much greater variability seen for results using observed climate data.

Temporal and spatial variability of rainfall over Greece

Recent studies have showed that there is a significant decrease in rainfall over Greece during the last half of the pervious century, following an overall decrease of the precipitation at the eastern Mediterranean. However, during the last decade an increase in rainfall was observed in most regions of the country, contrary to the general circulation climate models forecasts. An updated high-resolution dataset of monthly sums and annual daily maxima records derived from 136 stations during the period 1940–2012 allowed us to present some new evidence for the observed change and its statistical significance. The statistical framework used to determine the significance of the slopes in annual rain was not limited to the time independency assumption (Mann-Kendall test), but we also investigated the effect of short- and long-term persistence through Monte Carlo simulation. Our findings show that (a) change occurs in different scales; most regions show a decline since 1950, an increase since 1980 and remain stable during the last 15 years; (b) the significance of the observed decline is highly dependent to the statistical assumptions used; there are indications that the Mann-Kendall test may be the least suitable method; and (c) change in time is strongly linked with the change in space; for scales below 40 years, relatively close regions may develop even opposite trends, while in larger scales change is more uniform.

A spatial temporal downscaling approach to development of IDF relations for Perth airport region in the context of climate change

ABSTRACTDownscaling of climate projections is the most adapted method to assess the impacts of climate change at regional and local scales. This study utilized both spatial and temporal downscaling approaches to develop intensity–duration–frequency (IDF) relations for sub-daily rainfall extremes in the Perth airport area. A multiple regression-based statistical downscaling model tool was used for spatial downscaling of daily rainfall using general circulation models (GCMs) (Hadley Centre’s GCM and Canadian Global Climate Model) climate variables. A simple scaling regime was identified for 30 minutes to 24 hours duration of observed annual maximum (AM) rainfall. Then, statistical properties of sub-daily AM rainfall were estimated by scaling an invariant model based on the generalized extreme value distribution. RMSE, Nash-Sutcliffe efficiency coefficient and percentage bias values were estimated to check the accuracy of downscaled sub-daily rainfall. This proved the capability of the proposed approach in developing a linkage between large-scale GCM daily variables and extreme sub-daily rainfall events at a given location. Finally IDF curves were developed for future periods, which show similar extreme rainfall decreasing trends for the 2020s, 2050s and 2080s for both GCMs.Editor M.C. Acreman; Associate editor S. Kanae

Spectral Characteristics of Instrumental and Climate Model Surface Temperatures

Abstract The spatiotemporal temperature variability for several gridded instrumental and general circulation climate model data is characterized, contrasting power spectra of local and global temperatures, land and sea temperatures, and temperatures of different regions. There is generally a high degree of agreement between the spectral characteristics of instrumental and climate model data. All but the equatorial spectra exhibit a power-law shape and are hence more consistent with the spectra expected from long-memory processes than from short-memory processes. The power-law exponent β of the spectra is a measure of memory, or persistence, of the temperatures and is observed to be about twice as large for global temperature than for local temperatures. However, there are large variations, in particular between land and sea surface temperatures. This is shown by estimates of the spectra for different regions and global maps of β. It is also demonstrated that global spectra are related to local spectra via teleconnections between local temperatures.

The effects of future nationwide forest transition to discharge in the 21st century with regard to general circulation model climate change scenarios

Forest disturbance (or land-cover change) and climatic variability are commonly recognised as two major drivers interactively influencing hydrology in forested watersheds. Future climate changes and corresponding changes in forest type and distribution are expected to generate changes in rainfall runoff that pose a threat to river catchments. It is therefore important to understand how future climate changes will effect average rainfall distribution and temperature and what effect this will have upon forest types across Japan. Recent deforestation of the present-day coniferous forest and expected increases in evergreen forest are shown to influence runoff processes and, therefore, to influence future runoff conditions. We strongly recommend that variations in forest type be considered in future plans to ameliorate projected climate changes. This will help to improve water retention and storage capacities, enhance the flood protection function of forests, and improve human health. We qualitatively assessed future changes in runoff including the effects of variation in forest type across Japan. Four general circulation models (GCMs) were selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to provide the driving fields: the Model for Interdisciplinary Research on Climate (MIROC), the Meteorological Research Institute Atmospheric General Circulation Model (MRI-GCM), the Hadley Centre Global Environment Model (HadGEM), and the Geophysical Fluid Dynamics Laboratory (GFDL) climate model. The simulations consisted of an ensemble including multiple physics configurations and different reference concentration pathways (RCP2.6, 4.5, and 8.5), the results of which have produced monthly data sets for the whole of Japan. The impacts of future climate changes on forest type in Japan are based on the balance amongst changes in rainfall distribution, temperature and hydrological factors. Methods for assessing the impact of such changes include the Catchment Simulator modelling frameworks based on the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) model, which was expanded to estimate discharge by incorporating the effects of forest-type transition across the whole of Japan. The results indicated that, by the 2090s, annual runoff will increase above present-day values. Increases in annual variation in runoff by the 2090s was predicted to be around 14.1% when using the MRI-GCM data and 44.4% when using the HadGEM data. Analysis by long-term projection showed the largest increases in runoff in the 2090s were related to the type of forest, such as evergreen. Increased runoff can have negative effects on both society and the environment, including increased flooding events, worsened water quality, habitat destruction and changes to the forest moisture-retaining function. Prediction of the impacts of future climate change on water generation is crucial for effective environmental planning and management.

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections.

A dynamical downscaling method for probabilistic regional‐scale climate change projections was developed to cover the inherent uncertainty associated with multiple general circulation model (GCM) climate simulations. The climatological increments estimated by GCM results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding them to reanalysis data. The incremental handling of GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. For the probabilistic analysis, three values of a climatological variable simulated by RCMs for a mode were analyzed under an assumption of linear response to the multiple modal perturbations.

Twenty-First-Century Snowfall and Snowpack Changes over the Southern California Mountains

Abstract Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.