Using a Scenario-Neutral Framework to Avoid Potential Maladaptation to Future Flood Risk.

This paper examines changes in the strength of the Walker circulation (WC) using the pressure difference between the western and eastern equatorial Pacific. Changes in observations and in 35 climate models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) are determined. On the one hand, 78% of the models show a weakening of the WC over the twentieth century, consistent with the observations and previous studies using CMIP phase 3 (CMIP3) models. However, the observations also exhibit a strengthening in the last three decades (i.e., from 1980 to 2012) that is statistically significant at the 95% level. The models, on the other hand, show no consensus on the sign of change, and none of the models shows a statistically significant strengthening over the same period. While the reasons for the inconsistency between models and observations is not fully understood, it is shown that the ability of the models to generate trends as large as the observed from internal variability is reduced because most models have weaker than observed levels of both multidecadal variability and persistence of interannual variability in WC strength. In the twenty-first-century future projections, the WC weakens in 25 out of 35 models, under representative concentration pathway (RCP) 8.5, 9 out of 11 models under RCP6.0, 16 out of 18 models under RCP4.5, and 12 out of 15 models under RCP2.6. The projected decrease is also consistent with results obtained previously using models from CMIP3. However, as the reasons for the inconsistency between modeled and observed trends in the last three decades are not fully understood, confidence in the model projections is reduced.

Understanding future streamflow and changes to hydrological processes is one of the common uses of hydrological models. But the uncertainty of different warming scenarios, boundary conditions and extent of Earth System Models (ESMs) impacts how future conditions might translate into changes in hydrological processes and streamflow. We hypothesise that the coarse spatial resolution of readily available CORDEX‐CORE simulations (25 km) will either under‐ or overestimate streamflow in small, mesoscale, heterogeneous catchments (< 700 km 2 ), relative to hydrological models driven by local station data, due to the inability of projections to capture steep headwater‐valley climate gradients. Additionally, we test whether the selection of ESMs exerts a stronger influence on the sensitivity of hydrological model parameters than the choice of downscaling method. We focus on the near future (2024–2053) because projections for the far future (e.g., 2100) carry even greater uncertainty. To evaluate these hypotheses, we applied the J2000 rainfall‐runoff model to the Eerste River catchment using the Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios for the Coupled Model Intercomparison Project Phase 5 (CMIP5) for 2006–2053. The simulations were driven by both CORDEX‐CORE ESMs as well as local climate station data to allow for performance benchmarking. The results of the application highlight the reduced skill of J2000 when driven by projected input data compared to using local station data, particularly in simulating high flows. The most likely cause of this is the inability of the coarse resolution of CORDEX‐CORE (25 km) to capture the vast differences in climate between the headwater and valley within the Eerste, which is spatially variable. Using the CORDEX‐CORE projections as input into the hydrological model, there were, however, decent representations of simulated low flows. The sensitivity analysis showed that J2000 model parameters were more influenced by the selection of the ESMs than downscaling using the Regional Spectral Models, highlighted by more sensitivity to scaling of the air capacity of the soil module. Exploratory analyses of future hydrological conditions suggest a mean annual flow reduction of 10%–22% (Q1–Q3) under RCP 2.6 compared to 8%–26% (Q1–Q3) under RCP 8.5 for the near future (2024–2053). While most (66%) projections suggest that the high flows will likewise be reduced, there is a low percentage (33%) of projections that suggest an increase in high flows under RCP 8.5 for the near future. Many of the projections suggest a further extension of the dry season (66%) and the wet season could be moved by 1–2 months later, compared to the historical conditions where June typically represents the mid‐winter period. The simulated hydrological flows suggest a reduction in baseflow and interflow, but high‐resolution projections (≈5–10 km) are required to improve the agreement in the historical projections compared to observed data.

Assessment of future water availability from the Himalayan watersheds of Indus Basin (Jhelum, Kabul and upper Indus basin—UIB) is a growing concern for safeguarding the sustainable socioeconomic wellbeing downstream. This requires, before all, robust climate change information from the present-day state-of-the-art climate models. However, the robustness of climate change projections highly depends upon the fidelity of climate modeling experiments. Hence, this study assesses the fidelity of seven dynamically refined (0.44^{circ }) experiments, performed under the framework of the coordinated regional climate downscaling experiment for South Asia (CX-SA), and additionally, their six coarse-resolution driving datasets participating in the coupled model intercomparison project phase 5 (CMIP5). We assess fidelity in terms of reproducibility of the observed climatology of temperature and precipitation, and the seasonality of the latter for the historical period (1971–2005). Based on the model fidelity results, we further assess the robustness or uncertainty of the far future climate (2061–2095), as projected under the extreme-end warming scenario of the representative concentration pathway (RCP) 8.5. Our results show that the CX-SA and their driving CMIP5 experiments consistently feature low fidelity in terms of the chosen skill metrics, suggesting substantial cold (6–10 ^{circ }C) and wet (up to 80%) biases and underestimation of observed precipitation seasonality. Surprisingly, the CX-SA are unable to outperform their driving datasets. Further, the biases of CX-SA and of their driving CMIP5 datasets are higher in magnitude than their projected changes under RCP8.5—and hence under less extreme RCPs—by the end of 21st century, indicating uncertain future climates for the Indus Basin watersheds. Higher inter-dataset disagreements of both CMIP5 and CX-SA for their simulated historical precipitation and for its projected changes reinforce uncertain future wet/dry conditions whereas the CMIP5 projected warming is less robust owing to higher historical period uncertainty. Interestingly, a better agreement among those CX-SA experiments that have been obtained through downscaling different CMIP5 experiments with the same regional climate model (RCM) indicates the RCMs’ ability of modulating the influence of lateral boundary conditions over a large domain. These findings, instead of suggesting the usual skill-based identification of ’reasonable’ global or regional low fidelity experiments, rather emphasize on a paradigm shift towards improving their fidelity by exploiting the potential of meso-to-local scale climate models—preferably of those that can solely resolve global-to-local scale climatic processes—in terms of microphysics, resolution and explicitly resolved convections. Additionally, an extensive monitoring of the nival regime within the Himalayan watersheds will reduce the observational uncertainty, allowing for a more robust fidelity assessment of the climate modeling experiments.

Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: A Mediterranean hot spot case, Turkey

Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

The coffee crop is of utmost importance and a large aspect of the culture in Chiapas, Mexico and will most likely be affected by climate change in the near future. This research provides analysis of Coupled Model Intercomparison Project Phase Five (CMIP5) downscaled data, while the majority of work completed on the effects of climate change on the coffee crop in Chiapas has used CMIP3 data. Results are analyzed over the timeframe of 1950 to 2044 at Chiapas, Mexico. Trend analysis for maximum and minimum temperature (Tasmax and Tasmin) and precipitation for Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 was conducted. Using observational data and the data obtained from the model runs, in 2044, Tasmax (Tasmin) are predicted to be approximately 26.16°C (13.59°C) at RCP 4.5 and 27.81˚C (14.16˚C) at RCP 8.5. This is approximately 0.8°C (0.8°C) higher for Tasmax (Tasmin) at RCP 4.5 and approximately 2.5°C (1.35°C) higher for Tasmax (Tasmin) at RCP 8.5 than the average decadal temperature recorded in the 2000s. The conclusion of the analysis suggests that temperatures in 2044 will possibly be too high at RCP 4.5 and will very likely be too high at RCP 8.5 for the coffee crop to thrive within its natural growing temperature range (Tasmin to Tasmax) of 15˚C to 24˚C.

The assessment of compound flood risk often relies on the assumption that the dependence structure between flood drivers (e.g., rainfall intensity, coastal water levels, and streamflow) remains stationary under changing climatic conditions. Yet, traditional approaches that inherently assume stationary dependencies, or rely solely on historical relationships, may misrepresent flood risk and fail to identify hotspots of emerging infrastructure vulnerabilities. This study aims to (a) characterize the dependence structure between compound flood drivers using a parsimonious parametric framework, and (b) explore potential changes in this structure under future climate scenarios, by leveraging outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) regional climate projections. An ensemble of synthetic and historical storms is employed to simulate flood impacts across the Greater Boston region, forming the basis for statistically modeling the conditional dependence of the main flood drivers. Changes in the marginal distributions of these drivers, informed by CMIP6 simulations under various Representative Concentration Pathways (RCPs), are also integrated into the dependence framework to evaluate future trajectories of compound flood risk. The findings focus on determining whether shifts in the dependence structure offer a more nuanced understanding of evolving flood risk profiles, as well as identifying areas where traditional stationary assumptions may result in systematic errors. Ultimately, the study advances understanding of the dynamic interplay between flood drivers under future climate scenarios, and supports the development of adaptation strategies for regions vulnerable to compound flooding.

Climatic changes have altered hydrological and climatic parameters worldwide, and climate projections suggest that such alterations will continue. In order to maintain the sustainable development and acquire the knowledge of water availability, climatic projection must be coupled with hydrological models. In this study, Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models output were integrated with a calibrated hydrological model, Soil and Water Assessment Tools (SWAT) to evaluate the potential effect of climate change on green and blue water over Upper Narmada river Basin (UNB). Therefore, top three representative climate models (MIROC5, CNRM-CM5 and MPI-ESM-LR) from 24 CMIP5 climate models were selected for hydrological modelling. Selected representative climate model outputs were bias corrected by distribution mapping to remove systematic bias correction. Multi-site model calibration approaches indicated Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) as 0.77 and 0.76 for calibration (1978–1995), and 0.73 and 0.70 for validation (1996–2005), respectively. Calibrated model was run for baseline period (1970–2000) and three futuristic period P1 (2011–2040), P2 (2041–2070) and P3 (2071–2100) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Results indicated annual precipitation decreasing under RCP4.5 and RCP8.5 scenarios changes in green and blue water varying from 16.22 to −14.10% (CNRM,P3) under RCP4.5 and from 38.25 to −22.57% under RCP8.5 with reference to baseline scenario. This study established the sensitivity of UNB to future climatic changes employing projections from CMIP5 climate models and exhibited an approach that applied multiple climate model outputs to estimate potential change over the river basin.

The impact and uncertainty of climate change on stream flow of the Bilate River Watershed was assessed. Ensemble of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) under two Representative Concentration Pathways and six GCM structures were selected to form 24 future climate scenarios for the watershed. Soil and Water Assessment Tool (SWAT) model was selected to simulate stream flow of the watershed. The respective statistical results of the coefficient of determination (R2), Nash-Sutcliffe coefficient (NSE) and percent bias (PB) are 0.79, 0.78 and 0.56 for calibration period and 0.64, 0.60 and -21.7 for validation period which show that the model predicted the stream flow reasonably. The annual stream flow increased progressively throughout the century for all time periods. The increases under RCP 8.5 scenario are the larger compared to RCP 4.5 scenarios, approximately 42.42% during the 2080s period. The six GCMs selected to see the uncertainties related to GCMs suggest that the river flow will change by small amounts of -6.18 to 7.83% change compared with the baseline. The simulated runoff depended on the projected amount of rainfall embedded in the GCM structures selected to simulate the future climate and less dependent on the local temperature increment. Key words: Climate change, Bilate River watershed, stream flow, soil and water assessment tool (SWAT), uncertainty.

The application of two distinctively different hydrologic models, (conceptual-HBV) and (distributed-BTOPMC), was compared to simulate the future runoff across three unregulated catchments of the Australian Hydrologic Reference Stations (HRSs), namely Harvey catchment in WA, and Beardy and Goulburn catchments in NSW. These catchments have experienced significant runoff reduction during the last decades due to climate change and human activities. The Budyko-elasticity method was employed to assign the influences of human activities and climate change on runoff variations. After estimating the contribution of climate change in runoff reduction from the past runoff regime, the downscaled future climate signals from a multi-model ensemble of eight global climate models (GCMs) of the Coupled Model Inter-comparison Project phase-5 (CMIP5) under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios were used to simulate the future daily runoff at the three HRSs for the mid-(2046–2065) and late-(2080–2099) 21st-century. Results show that the conceptual model performs better than the distributed model in capturing the observed streamflow across the three contributing catchments. The performance of the models was relatively compatible in the overall direction of future streamflow change, regardless of the magnitude, and incompatible regarding the change in the direction of high and low flows for both future climate scenarios. Both models predicted a decline in wet and dry season's streamflow across the three catchments.

Ozone changes and associated climate impacts in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations are analyzed over the historical (1960–2005) and future (2006–2100) period under four Representative Concentration Pathways (RCP). In contrast to CMIP3, where half of the models prescribed constant stratospheric ozone, CMIP5 models all consider past ozone depletion and future ozone recovery. Multimodel mean climatologies and long‐term changes in total and tropospheric column ozone calculated from CMIP5 models with either interactive or prescribed ozone are in reasonable agreement with observations. However, some large deviations from observations exist for individual models with interactive chemistry, and these models are excluded in the projections. Stratospheric ozone projections forced with a single halogen, but four greenhouse gas (GHG) scenarios show largest differences in the northern midlatitudes and in the Arctic in spring (~20 and 40 Dobson units (DU) by 2100, respectively). By 2050, these differences are much smaller and negligible over Antarctica in austral spring. Differences in future tropospheric column ozone are mainly caused by differences in methane concentrations and stratospheric input, leading to ~10 DU increases compared to 2000 in RCP 8.5. Large variations in stratospheric ozone particularly in CMIP5 models with interactive chemistry drive correspondingly large variations in lower stratospheric temperature trends. The results also illustrate that future Southern Hemisphere summertime circulation changes are controlled by both the ozone recovery rate and the rate of GHG increases, emphasizing the importance of simulating and taking into account ozone forcings when examining future climate projections.

Surface runoff is a major water resource component and its spatial and temporal variations significantly impact on regional socio-economic development. In this study, changes to surface runoff in Jiangsu Province, China, were simulated using the variable infiltration capacity (VIC) model for the period 2011–2040, using input data from five Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models and three different Representative Concentration Pathway (RCP) scenarios. In general, annual mean precipitation under the five models and three scenarios showed a fluctuating upward trend, while annual mean temperatures were projected to increase by up to 1.34 °C, as compared with the reference period (1970–1999). Monthly mean runoff depths were generally predicted to increase, with the most significant increases occurring in December. Increasing runoff depths were highest under the RCP8.5 emission scenario and lowest under RCP4.5. The results of this study provide an important reference for policymakers planning for the future water resources in Jiangsu Province. Furthermore, we conducted a case study with the VIC model and it showed high consistency with gauges and provides a new reference for the studies of other plain regions.

Copula-based probabilistic characterization of precipitation extremes over North Sikkim Himalaya

Abstract. Warming trends in the Laurentian Great Lakes and surrounding areas have been observed in recent decades, and concerns continue to rise about the pace and pattern of future climate change over the world's largest freshwater system. To date, most regional climate models used for Great Lakes projections either neglected the lake-atmosphere interactions or are only coupled with a 1-D column lake model to represent the lake hydrodynamics. This study presents a Great Lakes climate change projection that has employed the two-way coupling of a regional climate model with a 3-D lake model (GLARM) to resolve 3-D hydrodynamics essential for large lakes. Using the three carefully selected Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs), we show that the GLARM ensemble average substantially reduces surface air temperature and precipitation biases of the driving GCM ensemble average in present-day climate simulations. The improvements are not only displayed from an atmospheric perspective but are also evident in the accurate simulations of lake temperature and ice coverage. We further present the GLARM projected climate change for the mid-21st century (2030–2049) and the late 21st century (2080–2099) in the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios. Under RCP 8.5, the Great Lakes basin is projected to warm by 1.3–2.1 ∘C by the mid-21st century and 4.1–5.0 ∘C by the end of the century relative to the early century (2000–2019). Moderate mitigation (RCP 4.5) reduces the mid-century warming to 0.8–1.8 ∘C and late-century warming to 1.8–2.7 ∘C. Annual precipitation in GLARM is projected to increase for the entire basin, varying from 0 % to 13 % during the mid-century and from 9 % to 32 % during the late century in different scenarios and simulations. The most significant increases are projected in spring and fall when current precipitation is highest and a minimal increase in winter when it is lowest. Lake surface temperatures (LSTs) are also projected to increase across the five lakes in all of the simulations, but with strong seasonal and spatial variability. The most significant LST increases occur in Lakes Superior and Ontario. The strongest warming is projected in spring that persists into the summer, resulting from earlier and more intense stratification in the future. In addition, diminishing winter stratification in the future suggests the transition from dimictic lakes to monomictic lakes by the end of the century. In contrast, a relatively smaller increase in LSTs during fall and winter is projected with heat transfer to the deep water due to the strong mixing and energy required for ice melting. Correspondingly, the highest monthly mean ice cover is projected to reduce to 3 %–15 % and 10 %–40 % across the lakes by the end of the century in RCP 8.5 and RCP 4.5, respectively. In the coastal regions, ice duration is projected to decrease by up to 60 d.

Recent climatic trends have increased forest fire activity in Canada. This study aimed to evaluate how forest fire conditions might evolve across the Canadian boreal forests in the future and to inform discussions about the impact of climate change on fire danger and severity. I generated surfaces of daily climate conditions using daily observational data from meteorological stations across Canada from 1976 to 2014. Simulated daily values of the same climatic variables were obtained from four earth system models of the Coupled Model Intercomparison Project Phase 5 project (CMIP5) for the historical period 1976–2005. Daily climate values for 2006–2100, forced by three climate change scenarios, Representative Concentration Pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, were also simulated by the models. The simulated data were bias‐corrected and delta‐downscaled to project future trends in fire activity to assess the spatiotemporal variations of the potential impacts of climate change on forest fires. My results suggest fire danger and severity would increase in many Canadian boreal forests under RCP 8.5. The changes in fire conditions under RCP 2.6 were the least noteworthy; RCP 4.5 was associated with medium‐level changes.