What is he best known for?
The fields of study he is best known for:
- Ecology
- Climate change
- Climatology
Daniel R. Cayan spends much of his time researching Climatology, Climate change, Climate model, Precipitation and Snowmelt.
He has included themes like Snow and Streamflow in his Climatology study.
His Climate change research includes themes of Meteorology, Spring, Physical geography and Greenhouse gas.
His research in Climate model intersects with topics in Future climate, Downscaling, Global change, Hydrological modelling and General Circulation Model.
The concepts of his Precipitation study are interwoven with issues in Snowpack, Seasonality, Greenhouse effect, Flood myth and Ecosystem.
His Snowmelt research is multidisciplinary, incorporating perspectives in Trend surface analysis, Water year and Hydrometeorology.
His most cited work include:
- Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity (3499 citations)
- Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity (3499 citations)
- Changes toward Earlier Streamflow Timing across Western North America (1006 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Climatology, Climate change, Precipitation, Oceanography and Climate model.
His biological study spans a wide range of topics, including Snow, Streamflow and Atmospheric sciences.
His research integrates issues of Structural basin and Snowmelt in his study of Streamflow.
His Climate change research is multidisciplinary, incorporating elements of Flood myth, Physical geography and Greenhouse gas.
The various areas that Daniel R. Cayan examines in his Precipitation study include Range, Snowpack, Forcing, Surface runoff and General Circulation Model.
His research brings together the fields of Global change and Climate model.
He most often published in these fields:
- Climatology (75.27%)
- Climate change (35.05%)
- Precipitation (28.80%)
What were the highlights of his more recent work (between 2014-2021)?
- Climatology (75.27%)
- Precipitation (28.80%)
- Climate change (35.05%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Climatology, Precipitation, Climate change, Downscaling and Climate model.
His primary area of study in Climatology is in the field of El Niño Southern Oscillation.
His Precipitation study combines topics in areas such as Water year, Snowpack, Arid, Mediterranean climate and Data set.
His work carried out in the field of Climate change brings together such families of science as Watershed, Probability distribution, Hydrometeorology, Ecosystem and Physical geography.
His study in Physical geography is interdisciplinary in nature, drawing from both Snow field and Permafrost.
His studies in Climate model integrate themes in fields like Salt marsh, Root-mean-square deviation, Standard deviation, Mean squared error and Humidity.
Between 2014 and 2021, his most popular works were:
- The key role of dry days in changing regional climate and precipitation regimes (172 citations)
- A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013 (156 citations)
- Toward mountains without permanent snow and ice (153 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Climate change
- Meteorology
The scientist’s investigation covers issues in Climatology, Climate change, Precipitation, Hydrometeorology and Climate model.
His Climatology study incorporates themes from Global warming, Cloud cover and Downscaling.
His Climate change research incorporates themes from Probability distribution, Flood myth and Physical geography.
His Flood myth research incorporates elements of Snowmelt, Global change and Damages.
His work deals with themes such as Quantile, Hydrology, Data set and Mean radiant temperature, which intersect with Hydrometeorology.
The study incorporates disciplines such as Matching and Ecosystem in addition to Climate model.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
