scholarly journals CHANGES OF THE SURFACE AIR TEMPERATURE IN THE 20TH – 21ST CENTURIES IN THE ANTARCTIC PENINSULA REGION BASED ON CLIMATE MODELS’ DATА

2018 ◽  
pp. 52-65 ◽  
Author(s):  
S. Krakovska ◽  
◽  
L. Pysarenko ◽  
Keyword(s):  
Air Temperature ◽  
Antarctic Peninsula ◽  
Climate Models ◽  
Surface Air Temperature ◽  
The Antarctic

scholarly journals Recent variations in surface mass balance of the Antarctic Peninsula ice sheet

2004 ◽  
Vol 50 (169) ◽  
pp. 257-267 ◽  
Author(s):  
Elizabeth M. Morris ◽  
Robert Mulvaney
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Air Temperature ◽  
Antarctic Peninsula ◽  
Empirical Relation ◽  
Surface Air Temperature ◽  
Climatic Conditions ◽  
Temperature Record ◽  
Surface Mass ◽  
The Antarctic

AbstractOver the period 1972–98 the height of the snow surface at eight Antarctic sites in Palmer Land and on Alexander Island has been measured with respect to fixed points on local nunataks. From these data an empirical relation between height changes over a given period and three key variables has been derived. These variables are (i) the local mean annual surface air temperature, (ii) a regional estimate of energy available for melt over the period (derived from the nearby Rothera air-temperature record) and (iii) a regional estimate of accumulation over the period (derived from the nearby Gomez Nunatak ice-core accumulation record). Using this relation, the contribution of the Antarctic Peninsula to sea-level rise for warming from climatic conditions (averaged over the last 30 years) is estimated to be −0.006 ± 0.002 mm a−1 K−1. If recent warm conditions persist, however, and meltwater can run off to the sea, the contribution to sea-level rise from ablation is calculated to be 0.07 ± 0.02 mm a−1 K−1.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

Major increases projected in extreme surface melt events in Antarctica, even under a moderate emission scenario

2020 ◽  
Author(s):  
Sarah Feron ◽  
Raul Cordero
Keyword(s):  
Sea Level ◽  
Antarctic Peninsula ◽  
Climate Models ◽  
Emission Scenario ◽  
Global Climate ◽  
Global Climate Models ◽  
Reference Period ◽  
West Antarctica ◽  
Surface Melt ◽  
The Antarctic

<p>Surface Melt (SM) is one of the factors that contribute to sea level rise; surface meltwater draining through the ice and beneath Antarctic glaciers may cause acceleration in their flow towards the sea. Changes in the frequency of relatively warm days (including heatwaves) can substantially alter the SM variability, thus leading to extreme melting events. By using simulations from 13 Global Climate Models (GCMs) and according to a moderate representative concentration pathways (RCP4.5), here we show that the frequency of extreme SM events (SM90; according to the 90th percentile over the reference period 1961-1990) may significantly increase in coastal areas of West Antarctica; in particular in the Antarctic Peninsula. By the end of the century SM90 estimates are expected to increase from currently 0.10 kg/m2/day to about 0.45 kg/m2/day in the Antarctic Peninsula. Increments in SM90 estimates are not just driven by changes in the average SM, but also by the variability in SM. The latter is expected to increase by around 50% in the Antarctic Peninsula.</p>

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