scholarly journals Greenland temperature and precipitation over the last 20 000 years using data assimilation

2020 ◽  
Vol 16 (4) ◽  
pp. 1325-1346
Author(s):  
Jessica A. Badgeley ◽  
Eric J. Steig ◽  
Gregory J. Hakim ◽  
Tyler J. Fudge
Keyword(s):  
Data Assimilation ◽  
Spatial Patterns ◽  
Climate Model ◽  
Ice Core ◽  
Ice Sheet ◽  
Proxy Data ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations ◽  
Paleoclimate Data

Abstract. Reconstructions of past temperature and precipitation are fundamental to modeling the Greenland Ice Sheet and assessing its sensitivity to climate. Paleoclimate information is sourced from proxy records and climate-model simulations; however, the former are spatially incomplete while the latter are sensitive to model dynamics and boundary conditions. Efforts to combine these sources of information to reconstruct spatial patterns of Greenland climate over glacial–interglacial cycles have been limited by assumptions of fixed spatial patterns and a restricted use of proxy data. We avoid these limitations by using paleoclimate data assimilation to create independent reconstructions of mean-annual temperature and precipitation for the last 20 000 years. Our method uses oxygen isotope ratios of ice and accumulation rates from long ice-core records and extends this information to all locations across Greenland using spatial relationships derived from a transient climate-model simulation. Standard evaluation metrics for this method show that our results capture climate at locations without ice-core records. Our results differ from previous work in the reconstructed spatial pattern of temperature change during abrupt climate transitions; this indicates a need for additional proxy data and additional transient climate-model simulations. We investigate the relationship between precipitation and temperature, finding that it is frequency dependent and spatially variable, suggesting that thermodynamic scaling methods commonly used in ice-sheet modeling are overly simplistic. Our results demonstrate that paleoclimate data assimilation is a useful tool for reconstructing the spatial and temporal patterns of past climate on timescales relevant to ice sheets.

scholarly journals Greenland temperature and precipitation over the last 20,000 years using data assimilation

2020 ◽  
Author(s):  
Jessica A. Badgeley ◽  
Eric J. Steig ◽  
Gregory J. Hakim ◽  
Tyler J. Fudge
Keyword(s):  
Data Assimilation ◽  
Spatial Patterns ◽  
Climate Model ◽  
Model Simulation ◽  
Ice Core ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Temperature And Precipitation ◽  
Paleoclimate Data ◽  
Ice Core Records

Abstract. Reconstructions of past temperature and precipitation are fundamental to modeling the Greenland Ice Sheet and assessing its sensitivity to climate. Paleoclimate information is sourced from proxy records and climate-model simulations; however, the former are spatially incomplete while the latter are sensitive to model dynamics and boundary conditions. Efforts to combine these sources of information to reconstruct spatial patterns of Greenland climate over glacial-interglacial cycles have been limited by assumptions of fixed spatial patterns and a restricted use of proxy data. We avoid these limitations by using paleoclimate data assimilation to create independent reconstructions of temperature and precipitation for the last 20,000 years. Our method uses information from long ice-core records and extends it to all locations across Greenland using spatial relationships derived from a transient climate-model simulation. Our reconstructions evaluate well against independent ice-core records. In addition, we find that the relationship between precipitation and temperature is frequency dependent and spatially variable, suggesting that thermodynamic scaling methods commonly used in ice-sheet modeling are overly simplistic. Our results demonstrate that paleoclimate data assimilation is a useful tool for reconstructing the spatial and temporal patterns of past climate on timescales relevant to ice sheets.

scholarly journals Joint bias correction of temperature and precipitation in climate model simulations

2014 ◽  
Vol 119 (23) ◽  
pp. 13,153-13,162 ◽  
Author(s):  
Chao Li ◽  
Eva Sinha ◽  
Daniel E. Horton ◽  
Noah S. Diffenbaugh ◽  
Anna M. Michalak
Keyword(s):  
Bias Correction ◽  
Climate Model ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations

scholarly journals Recent Greenland Accumulation Estimated from Regional Climate Model Simulations and Ice Core Analysis*

2002 ◽  
Vol 15 (19) ◽  
pp. 2821-2832 ◽  
Author(s):  
K. Dethloff ◽  
M. Schwager ◽  
J. H. Christensen ◽  
S. Kiilsholm ◽  
A. Rinke ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Core ◽  
Core Analysis ◽  
Model Simulations ◽  
Climate Model Simulations

scholarly journals Increased variability in Greenland Ice Sheet runoff from satellite observations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Thomas Slater ◽  
Andrew Shepherd ◽  
Malcolm McMillan ◽  
Amber Leeson ◽  
Lin Gilbert ◽  
...  
Keyword(s):  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Model Simulations ◽  
Climate Model Simulations

AbstractRunoff from the Greenland Ice Sheet has increased over recent decades affecting global sea level, regional ocean circulation, and coastal marine ecosystems, and it now accounts for most of the contemporary mass imbalance. Estimates of runoff are typically derived from regional climate models because satellite records have been limited to assessments of melting extent. Here, we use CryoSat-2 satellite altimetry to produce direct measurements of Greenland’s runoff variability, based on seasonal changes in the ice sheet’s surface elevation. Between 2011 and 2020, Greenland’s ablation zone thinned on average by 1.4 ± 0.4 m each summer and thickened by 0.9 ± 0.4 m each winter. By adjusting for the steady-state divergence of ice, we estimate that runoff was 357 ± 58 Gt/yr on average – in close agreement with regional climate model simulations (root mean square difference of 47 to 60 Gt/yr). As well as being 21 % higher between 2011 and 2020 than over the preceding three decades, runoff is now also 60 % more variable from year-to-year as a consequence of large-scale fluctuations in atmospheric circulation. Because this variability is not captured in global climate model simulations, our satellite record of runoff should help to refine them and improve confidence in their projections.

scholarly journals Statistical Downscaling Using Localized Constructed Analogs (LOCA)*

2014 ◽  
Vol 15 (6) ◽  
pp. 2558-2585 ◽  
Author(s):  
David W. Pierce ◽  
Daniel R. Cayan ◽  
Bridget L. Thrasher
Keyword(s):  
Climate Model ◽  
Spatial Coherence ◽  
Weighted Average ◽  
Grid Cell ◽  
Local Area ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Climate Model Simulations ◽  
A New Technique ◽  
The One

Abstract A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.

Sign in / Sign up

Export Citation Format

Share Document