Temperature and precipitation in the context of the annual cycle over Asia: Model evaluation and future change

2017 ◽  
Vol 53 (2) ◽  
pp. 229-242 ◽  
Author(s):  
Suyeon Moon ◽  
Kyung-Ja Ha
Keyword(s):  
Annual Cycle ◽  
Model Evaluation ◽  
Future Change ◽  
Temperature And Precipitation

scholarly journals The Influence of Cloudiness on Air Temperature and Precipitation on the Territory of Poland (1951–2000)

2008 ◽  
Vol 13 (1) ◽  
pp. 89-103
Author(s):  
Elwira Żmudzka
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Annual Cycle ◽  
Linear Correlation ◽  
Temperature And Precipitation ◽  
Temporal Course ◽  
Precipitation And Temperature ◽  
Analysis Of Time Series

Abstract Analysis of time series was performed of cloudiness, air temperature and precipitation for the years 1951–2000, the data used representing lowland Poland. The properties of the temporal course of these elements of climate and the interrelations between their variability in time have been determined. The strength of dependence of precipitation and temperature upon the magnitude of cloudiness in the annual cycle has been assessed with the linear correlation coefficient. The essential climate-forming role of cloudiness has been confirmed. The magnitude of cloudiness over Poland explains up to 70% of variability of temperature and precipitation. These interrelations are the strongest in the warmer half of the year. Atmospheric circulation and cloudiness explain up to 85% of variability of the climate elements studied.

Gradual transition from temperature to precipitation controlled regime in Rhone River discharges

2020 ◽  
Author(s):  
Carla Taricco ◽  
Sara Rubinetti ◽  
Enrico Arnone ◽  
Davide Zanchettin ◽  
Angelo Rubino ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Annual Cycle ◽  
Large Scale ◽  
Gradual Transition ◽  
Rhône River ◽  
Temporal Behaviour ◽  
Temperature And Precipitation ◽  
Rhone River ◽  
Discharge Regime ◽  
The Impact

<p>River discharge series provide large-scale hydrological information over a broad range of timescales. Despite discharge records consist of punctual measurements, they integrate variations in snowmelting, precipitation and runoff processes over the catchment till the discharge measurement site.</p><p>Discharges of the Rhone River, one of the largest rivers in Europe, have been monitored accurately during the last century at different sites. Long discharge records from seven stations along the course reveal the spatial and temporal behaviour of discharges from the source of the river to its mouth.  An accurate spectral analysis of the records, performed using advanced spectral analysis methods, allow us to extract significant periodic variations in the records at different temporal scales. Then, we analyse the sensitivity of such periodic variations to evolving hydroclimate conditions, in particular focusing on the relationship between discharge and temperature and precipitation.</p><p>The strong annual oscillation recorded at stations close to the source is almost entirely due to snow melting on alpine glaciers, closely resembling the temperature annual cycle. This remarkable agreement allows to consider the upstream discharges as a thermometer on the glacier region during the melting season. On the contrary, the decrease of the annual cycle going towards the mouth of the river and the contemporary growth of interannual components demonstrates the transition from a temperature to a precipitation controlled discharge regime.</p><p>We will finally discuss the impact of large-scale variability patterns on the detected discharge variations and associated implications for their near-term predictability.</p>

scholarly journals Seasonal transition dates can reveal biases in Arctic sea ice simulations

2020 ◽  
Author(s):  
Abigail Smith ◽  
Alexandra Jahn ◽  
Muyin Wang
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
Model Evaluation ◽  
Climate Model ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Seasonal Transition ◽  
Climate Model Evaluation ◽  
Arctic Sea ◽  
Break Up

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, break-up, opening, freeze onset, freeze-up and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1.5–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions of melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

Fidelity of CORDEX experiments over Himalayan Watersheds

2020 ◽  
Author(s):  
Shabehul Hasson
Keyword(s):  
Sustainable Development ◽  
Water Availability ◽  
Historical Period ◽  
Climate Modelling ◽  
Future Change ◽  
Impact Studies ◽  
Changing Climate ◽  
Temperature And Precipitation ◽  
Precipitation Changes ◽  
The Impact

<p>How much water will come in future from the Himalayan watersheds under changing climate is a growing concern for ensuring sustainable development of downstream agrarian economies and for socioeconomic wellbeing of dependent communities. However, robust assessment of future water availability largely depends upon fidelity of climate modelling experiments simulating future change scenarios, beyond the debate of their possibility and plausibility. Thus, I assess the fidelity of CORDEX experiments over the Himalayan watersheds for the historical period against a broader set of observational datasets, in terms of reproducibility of the observed climatology of temperature and precipitation. Changes in these basic variables relevant for impact studies will also be presented under different scenarios and their robustness will be discussed in view of their fidelity for the historical period. The study will suggest the suitability of CORDEX experiments for the impact studies and further possibilities for improvement.</p>

scholarly journals Seasonal transition dates can reveal biases in Arctic sea ice simulations

2020 ◽  
Vol 14 (9) ◽  
pp. 2977-2997
Author(s):  
Abigail Smith ◽  
Alexandra Jahn ◽  
Muyin Wang
Keyword(s):  
Sea Ice ◽  
Annual Cycle ◽  
Model Evaluation ◽  
Climate Model ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Seasonal Transition ◽  
Ice Growth ◽  
Climate Model Evaluation ◽  
Arctic Sea

Abstract. Arctic sea ice experiences a dramatic annual cycle, and seasonal ice loss and growth can be characterized by various metrics: melt onset, breakup, opening, freeze onset, freeze-up, and closing. By evaluating a range of seasonal sea ice metrics, CMIP6 sea ice simulations can be evaluated in more detail than by using traditional metrics alone, such as sea ice area. We show that models capture the observed asymmetry in seasonal sea ice transitions, with spring ice loss taking about 1–2 months longer than fall ice growth. The largest impacts of internal variability are seen in the inflow regions for melt and freeze onset dates, but all metrics show pan-Arctic model spreads exceeding the internal variability range, indicating the contribution of model differences. Through climate model evaluation in the context of both observations and internal variability, we show that biases in seasonal transition dates can compensate for other unrealistic aspects of simulated sea ice. In some models, this leads to September sea ice areas in agreement with observations for the wrong reasons.

scholarly journals The Seasonal Correlation Between Temperature and Precipitation Over Korea and Europe and the Future Change From RCP8.5 Scenario

Atmosphere ◽  
2017 ◽  
Vol 27 (1) ◽  
pp. 79-91 ◽  
Author(s):  
Jin-Uk Kim ◽  
Kyung-On Boo ◽  
Sungbo Shim ◽  
Won-Tae Kwon ◽  
Young-Hwa Byun
Keyword(s):  
Future Change ◽  
Temperature And Precipitation ◽  
The Future ◽  
Seasonal Correlation

scholarly journals Future Change Using the CMIP5 MME and Best Models: I. Near and Long Term Future Change of Temperature and Precipitation over East Asia

Atmosphere ◽  
2014 ◽  
Vol 24 (3) ◽  
pp. 403-417 ◽  
Author(s):  
Hyejin Moon ◽  
Byeong-Hee Kim ◽  
Hyoeun Oh ◽  
June-Yi Lee ◽  
Kyung-Ja Ha
Keyword(s):  
East Asia ◽  
Future Change ◽  
Temperature And Precipitation

scholarly journals Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 996 ◽  
Author(s):  
Stephanie Gleixner ◽  
Teferi Demissie ◽  
Gulilat Tefera Diro
Keyword(s):  
Spatial Distribution ◽  
East Africa ◽  
Annual Cycle ◽  
Regional Analysis ◽  
Climate Data ◽  
Near Surface ◽  
Temperature And Precipitation ◽  
Weather And Climate ◽  
Long Term Trends

Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources.

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