Results 71 to 80 of about 11,795 (298)
Gaoyuan qixiangUsing effective bias correction methods and transforming non-stationary data to stationary can enhance the scientific accuracy of temperature analysis， allowing for a deeper understanding of its temporal and spatial distribution characteristics and ...
Xue ZHANG +6 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
Improved representation of extratropical cyclone structure in HighResMIP models [PDF]
, 2022 This is the final version. Available on open access from Wiley via the DOI in this recordData availability: ERA5 reanalysis is available from the Copernicus Climate Change Service Climate Data Store (https://doi.org/10.24381/cds.bd0915c6).
Priestley, MDK, Catto, JL
core +1 more source
International Journal of Climatology, EarlyView.This study investigates summer Greenland atmospheric blocking using the Lagrangian tracking Python package blocktrack applied to ERA5 reanalysis and CMIP6 models. Two types of blocks—upstream (Northern Canada origin) and retrograding (Northern Atlantic origin)—are distinguished, with upstream blocks driving recent observed frequency increasing trends ...
Michele Filippucci +2 more
wiley +1 more source
, 2019 Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: ScenarioMIP. These data include all datasets published for 'CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id ...
Danilov, Sergey +9 more
core +1 more source
CMIP6.HighResMIP.NERC.HadGEM3-GC31-HM
, 2020 Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets.
Schiemann, Reinhard +3 more
core +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
Evolution of rain-flood characteristics and projection of future flood risk in Chongqing City
Shui kexue jinzhanUrban flooding，exacerbated by the dual impacts of global climate change and rapid urbanization，poses a significant threat to the sustainable development of cities.
Junliang JIN +5 more
doaj +1 more source
CMIP6 Data Citation of Evolving Data
Data Science Journal, 2017 Data citations have become widely accepted. Technical infrastructures as well as principles and recommendations for data citation are in place but best practices or guidelines for their implementation are not yet available. On the other hand, the scientific climate community requests early citations on evolving data for credit, e.g.
Martina Stockhause +1 more
openaire +4 more sources