Results 71 to 80 of about 11,256 (249)
International Journal of Climatology, EarlyView.We downscale CanESM2 with the Regional Climate Model version 4 (20 km) and evaluate against 15 stations to derive monthly PET (Hargreaves–Samani) and SPEI for northeastern Iran (1990–2009, 2040–2059, 2080–2099). Results show robust PET increases and a shift toward more negative monthly SPEI—especially April–September—indicating earlier onset and longer
Nasrin Salehnia +6 more
wiley +1 more source
Response of microbial decomposition to spin-up explains CMIP5 soil carbon range until 2100 [PDF]
Geoscientific Model Development, 2014 Soil carbon storage simulated by the Coupled Model Intercomparison Project (CMIP5) models varies 6-fold for the present day. Here, we confirm earlier work showing that this range already exists at the beginning of the CMIP5 historical simulations.
J.-F. Exbrayat +2 more
doaj +1 more source
Changing Water Resources in the Indus Basin: A Multi‐Model Budyko‐Based Analysis
International Journal of Climatology, EarlyView.Budyko‐based analysis evaluates historical (1962–2005) and future hydroclimatic change across the Indus River Basin. Budyko shifts indicate rising atmospheric evaporative demand and increasing energy limitation under future scenarios. ABSTRACT Assessing hydroclimatic variability and future water availability is crucial for sustainable water‐resource ...
Muhammad Arif +3 more
wiley +1 more source
Representation of the Arctic Oscillation in the CMIP5 Models
Advances in Climate Change Research, 2013 The temporal variability and spatial pattern of the Arctic Oscillation (AO) simulated in the historical experiment of 26 coupled climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are evaluated. Spectral analysis of the monthly AO index indicates that 23 out of the 26 CMIP5 models exhibit no statistically ...
Jin-Qing, Zuo +2 more
openaire +2 more sources
The Atlantic ITCZ bias in CMIP5 models [PDF]
Climate Dynamics, 2014 Precipitation over the tropical Atlantic in 24 atmospheric models is analyzed using an object-based approach, which clusters rainy areas in the models as precipitation objects and calculates their properties such as size, amplitude, and location. Based on the distribution of precipitation objects over land and over ocean, two classes of models emerge ...
Siongco, A. +2 more
openaire +2 more sources
Reservoir yield intercomparison of large dams in Jaguaribe Basin-CE in climate change scenarios
RBRHClimate changes can have different impacts on water resources. Strategies to adapt to climate changes depend on impact studies. In this context, this study aimed to estimate the impact that changes in precipitation, projected by Global Circulation Models
Renato de Oliveira Fernandes +3 more
doaj +1 more source
Hydrology Research, 2022 Investigation of the role of multiple general circulation model (GCM) ensembles in obtaining comprehensive knowledge of hydrological responses across the Yellow River Basin (YRB), China, is still of substantial importance.
Yuxue Guo +5 more
doaj +1 more source
Characterising City Scale Heat Climatology for Australian Climate Zones
International Journal of Climatology, EarlyView.The Urban Heat Island (UHI) effect in Australian cities peaks at night, intensifying under dry conditions, clear skies, increased solar radiation, and low wind speeds. Long‐term observations show that nighttime temperatures in major cities have warmed faster than their rural surroundings and future climate projections fail to capture this disparity ...
Vihan C. N. Weeraratne +4 more
wiley +1 more source
Geoscience Letters, 2023 Previous studies have revealed little progress in the ensemble mean of Coupled Model Intercomparison Project Phase 6 (CMIP6) models compared to Phase 5 (CMIP5) models in simulating global dynamic sea level (DSL).
Chenyang Jin, Hailong Liu, Pengfei Lin
doaj +1 more source
Permafrost and Periglacial Processes, EarlyView.ABSTRACT Due to its subsurface nature, permafrost cannot be directly observed with the naked eye or optical remote sensing. Consequently, accurately describing its distribution and thermal state is challenging. This is especially true in vast, remote environments, where obtaining comprehensive field data is demanding or improbable.
Ria Nicholson +5 more
wiley +1 more source