The influence of large-scale atmospheric circulation weather types on variations in the water level of Lake Urmia, Iran

2011 ◽  
Vol 32 (13) ◽  
pp. 1990-1996 ◽  
Author(s):  
Sheida Jalili ◽  
Ingo Kirchner ◽  
David M. Livingstone ◽  
Saeid Morid
Keyword(s):  
Water Level ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Lake Urmia ◽  
Weather Types ◽  
Large Scale Atmospheric Circulation

Transition probabilities between synoptic weather types as a fingerprint for climate model evaluation

2021 ◽  
Author(s):  
Juan Antonio Fernandez-Granja ◽  
Ana Casanueva ◽  
Joaquín Bedia ◽  
Jesús Fernández
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Weather Types ◽  
Observational Uncertainty ◽  
Leibler Divergence ◽  
Synoptic Conditions ◽  
Large Scale Atmospheric Circulation

<p>Global Climate Models (GCMs) generally exhibit significant biases in the representation of large-scale atmospheric circulation. Even after bias adjustment, these errors remain and are inherited to some extent by the derived downscaling products, impairing the credibility of future regional projections. </p><p>We perform a process-based evaluation of state-of-the-art GCMs from CMIP5 and CMIP6, with a focus on the simulation of the synoptic climatological patterns having a most prominent effect on the European climate. To this aim, we use the Lamb Weather Type Classification (LWT, Lamb, 1972). We undertake a comprehensive assessment based on several evaluation measures, such as Kullback-Leibler divergence (KL), Relative Bias and Transition Probability Matrix Score (TPMS), used to assess the ability of the GCMs in reproducing not only the frequencies of the different Lamb Weather Types (LWTs), but also the daily probabilities of transitions among them. We show that the novel TPMS score poses a stringent test on the GCM performance, allowing for a convenient model ranking based on each model’s transition probability matrix fingerprint. Deficiencies in the transition probabilities from one LWT to another might explain the misrepresentation of the synoptic conditions and their frequencies by the GCMs. Four different reanalysis products of varying characteristics are considered as pseudo-observational reference in order to assess observational uncertainty. </p><p>Our results unveil an overall improvement of salient atmospheric circulation features of CMIP6 with respect to CMIP5, demonstrating the ability of the new models to better capture key synoptic conditions. The improvement is consistent across observational references, although it is uneven across models and large frequency biases still remain for the dominant LWTs in many cases. In particular, some CMIP6 models attain similar or even worse results than their CMIP5 counterparts. In light of the large differences found across models, we advocate for a careful selection of driving GCMs in downscaling experiments with a special focus on large-scale atmospheric circulation aspects.</p><p> </p>

scholarly journals Trends and regime shifts in climatic conditions and river runoff in Estonia during 1951–2015

2017 ◽  
Vol 8 (4) ◽  
pp. 963-976 ◽  
Author(s):  
Jaak Jaagus ◽  
Mait Sepp ◽  
Toomas Tamm ◽  
Arvo Järvet ◽  
Kiira Mõisja
Keyword(s):  
Time Series ◽  
Snow Cover ◽  
Atmospheric Circulation ◽  
Air Temperature ◽  
Large Scale ◽  
Regime Shifts ◽  
The Arctic ◽  
Dry Period ◽  
Snow Cover Duration ◽  
Large Scale Atmospheric Circulation

Abstract. Time series of monthly, seasonal and annual mean air temperature, precipitation, snow cover duration and specific runoff of rivers in Estonia are analysed for detecting of trends and regime shifts during 1951–2015. Trend analysis is realised using the Mann–Kendall test and regime shifts are detected with the Rodionov test (sequential t-test analysis of regime shifts). The results from Estonia are related to trends and regime shifts in time series of indices of large-scale atmospheric circulation. Annual mean air temperature has significantly increased at all 12 stations by 0.3–0.4 K decade−1. The warming trend was detected in all seasons but with the higher magnitude in spring and winter. Snow cover duration has decreased in Estonia by 3–4 days decade−1. Changes in precipitation are not clear and uniform due to their very high spatial and temporal variability. The most significant increase in precipitation was observed during the cold half-year, from November to March and also in June. A time series of specific runoff measured at 21 stations had significant seasonal changes during the study period. Winter values have increased by 0.4–0.9 L s−1 km−2 decade−1, while stronger changes are typical for western Estonia and weaker changes for eastern Estonia. At the same time, specific runoff in April and May have notably decreased indicating the shift of the runoff maximum to the earlier time, i.e. from April to March. Air temperature, precipitation, snow cover duration and specific runoff of rivers are highly correlated in winter determined by the large-scale atmospheric circulation. Correlation coefficients between the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indices reflecting the intensity of westerlies, and the studied variables were 0.5–0.8. The main result of the analysis of regime shifts was the detection of coherent shifts for air temperature, snow cover duration and specific runoff in the late 1980s, mostly since the winter of 1988/1989, which are, in turn, synchronous with the shifts in winter circulation. For example, runoff abruptly increased in January, February and March but decreased in April. Regime shifts in annual specific runoff correspond to the alternation of wet and dry periods. A dry period started in 1964 or 1963, a wet period in 1978 and the next dry period at the beginning of the 21st century.

scholarly journals Impact of climate change on groundwater point discharge: backflooding of karstic springs (Loiret, France)

2011 ◽  
Vol 8 (2) ◽  
pp. 2235-2262
Author(s):  
E. Joigneaux ◽  
P. Albéric ◽  
H. Pauwels ◽  
C. Pagé ◽  
L. Terray ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Groundwater Quality ◽  
Atmospheric Circulation ◽  
Large Scale ◽  
Weather Type ◽  
Future Climate Change ◽  
Impact Of Climate Change ◽  
The Impact ◽  
Large Scale Atmospheric Circulation

Abstract. Under certain hydrological conditions it is possible for spring flow in karst systems to be reversed. When this occurs, the resulting invasion by surface water, i.e. the backflooding, represents a serious threat to groundwater quality because the surface water could well be contaminated. Here we examine the possible impact of future climate change on the occurrences of backflooding in a specific karst system, having first established the occurrence of such events in the selected study area over the past 40 yr. It would appear that backflooding has been more frequent since the 1980s, and that it is apparently linked to river flow variability on the pluri-annual scale. The avenue that we adopt here for studying recent and future variations of these events is based on a downscaling algorithm relating large-scale atmospheric circulation to local precipitation spatial patterns. The large-scale atmospheric circulation is viewed as a set of quasi-stationary and recurrent states, called weather types, and its variability as the transition between them. Based on a set of climate model projections, simulated changes in weather-type occurrence for the end of the century suggests that backflooding events can be expected to decrease in 2075–2099. If such is the case, then the potential risk for groundwater quality in the area will be greatly reduced compared to the current situation. Finally, our results also show the potential interest of the weather-type based downscaling approach for examining the impact of climate change on hydrological systems.

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