Application of linear dynamical mode decomposition to surface air temperature in 20th century

2020 ◽  
Author(s):  
Andrey Gavrilov ◽  
Sergey Kravtsov ◽  
Dmitry Mukhin ◽  
Evgeny Loskutov ◽  
Alexander Feigin
Keyword(s):  
20Th Century ◽  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
State Of The Art ◽  
Surface Air Temperature ◽  
Climate Dynamics ◽  
Bayesian Optimization ◽  
Climate Signal ◽  
Function Decomposition

<p>According to recent study [1], the current state-of-the-art climate models lack the substantial part of internal multidecadal climate signal which is observed in the 20th century surface air temperature reanalysis data as a global stadium wave (GSW). In the presented work we further investigate this phenomenon using the recently developed method [2] of empirical spatio-temporal data decomposition into linear dynamical modes (LDMs). The important property of LDMs is their ability to take into account the time scales of the system evolution (they are extracted from observed dataset by the Bayesian optimization technique) better than some other linear techniques, e.g. traditional empirical orthogonal function decomposition. Like any linear decomposition, it provides the time series of principal components and corresponding spatial patterns.<br>We modify the initially developed LDM decomposition to make it possible to take into account a prescribed external forcing (like CO2 emissions, sun activity etc.) and then find part of variability which may be considered as an internal climate dynamics decomposed into set of modes with different time scales, and hence may be helpful in GSW interpretation. The results of applying the method to the 20th century surface air temperature with different ways of forcing inclusion will be presented and discussed.</p><p>1. Kravtsov, S., Grimm, C., & Gu, S. (2018). Global-scale multidecadal variability missing in state-of-the-art climate models. Npj Climate and Atmospheric Science, 1(1), 34. https://doi.org/10.1038/s41612-018-0044-6<br>2. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2018). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics. https://doi.org/10.1007/s00382-018-4255-7</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.

The Intriguing Relation Between Earth's Rotation, Geomagnetic Field, and Climate at Multidecadal Time Scales: Insights from NCEP 20th Century Reanalysis

2020 ◽  
Author(s):  
Sébastien Lambert
Keyword(s):  
Magnetic Field ◽  
20Th Century ◽  
Time Scales ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Cosmic Ray ◽  
Cloud Formation ◽  
Climate Variables ◽  
The Core ◽  
Earth Magnetic Field

<p>High correlations between length-of-day (LOD) and climate variables (sea-surface temperature, surface air temperature) have been pointed out in numerous studies (e.g., Lambeck and Cazenave 1976, Dickey et al. 2011, Marcus 2016) in the recent years at both decadal and multidecadal time scales. Moreover, the multidecal LOD variations (that reach several milliseconds) have been shown to have their origin in variations in the core angular momentum and are associated with variations of the Earth magnetic field now modeled back to the middle of the 19th century. Though the climate variations unlikely arise from the core, some authors suggested that they could result from modulation of incoming cosmic ray flux by Earth's magnetic field through cloud formation. In this study, we propose to check correlations between LOD, Earth dipolar magnetic field, and climate variables as taken from a century reanalysis (gridded air temperature and cloud coverage from NCEP 20th Century Reanalysis V2 and V3) in order to (i) confirm results of previous studies about a possible causality between geomagnetism, LOD, and climate, and (ii) locate the hot spots where the link between geomagnetism and cloud formation could be significant.</p>

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 Offsetting effects of aerosols on Arctic and global climate in the late 20th century

2014 ◽  
Vol 14 (8) ◽  
pp. 3969-3975 ◽  
Author(s):  
Q. Yang ◽  
C. M. Bitz ◽  
S. J. Doherty
Keyword(s):  
20Th Century ◽  
Air Temperature ◽  
Radiative Forcing ◽  
Global Climate ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Late 20Th Century ◽  
Climate Effect ◽  
Climate System Model ◽  
Arctic Surface

Abstract. We examine the impacts of atmospheric aerosols on Arctic and global climate using a series of 20th century transient simulations from Community Climate System Model version 4 (CCSM4). We focus on the response of surface air temperature to the direct radiative forcing driven by changes in sulfate and black carbon (BC) concentrations from 1975 to 2005 and we also examine the response to changes in sulfate, BC, and organic carbon (OC) aerosols collectively. The direct forcing from sulfate dominates the aerosol climate effect. Globally averaged, simultaneous changes in all three aerosols produce a cooling trend of 0.015 K decade−1 during the period 1975–2005. In the Arctic, surface air temperature has large spatial variations in response to changes in aerosol concentrations. Over the European Arctic, aerosols induce about 0.6 K decade−1 warming, which is about 1.8 K warming over the 30-year period. This warming is triggered mainly by the reduction in sulfate and BC emissions over Europe since the 1970s and is reinforced by sea ice loss and a strengthening in atmospheric northward heat transport. Changes in sulfate concentrations account for about two thirds of the warming and BC for the remaining one third. Over the Siberian and North American Arctic, surface air temperature is likely influenced by changes in aerosol concentrations over Asia. An increase in sulfate optical depth over Asia induces a large cooling while an increase in BC over Asia causes a significant warming.

Guess-Work and Reasonings on Centennial Evolution of Surface Air Temperature in Russia. Part III: Where is the Joint Between Norms and Hazards from a Bifurcation Analysis Viewpoint?

2016 ◽  
Vol 26 (07) ◽  
pp. 1650122 ◽  
Author(s):  
Yury Kolokolov ◽  
Anna Monovskaya
Keyword(s):  
Air Temperature ◽  
Bifurcation Analysis ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Climate Dynamics ◽  
Causal Effects ◽  
Bifurcation Diagrams ◽  
Local Climate ◽  
Climate Systems ◽  
Qualitative Changes

The paper continues the application of the bifurcation analysis in the research on local climate dynamics based on processing the historically observed data on the daily average land surface air temperature. Since the analyzed data are from instrumental measurements, we are doing the experimental bifurcation analysis. In particular, we focus on the discussion where is the joint between the normal dynamics of local climate systems (norms) and situations with the potential to create damages (hazards)? We illustrate that, perhaps, the criteria for hazards (or violent and unfavorable weather factors) relate mainly to empirical considerations from human opinion, but not to the natural qualitative changes of climate dynamics. To build the bifurcation diagrams, we base on the unconventional conceptual model (HDS-model) which originates from the hysteresis regulator with double synchronization. The HDS-model is characterized by a variable structure with the competition between the amplitude quantization and the time quantization. Then the intermittency between three periodical processes is considered as the typical behavior of local climate systems instead of both chaos and quasi-periodicity in order to excuse the variety of local climate dynamics. From the known specific regularities of the HDS-model dynamics, we try to find a way to decompose the local behaviors into homogeneous units within the time sections with homogeneous dynamics. Here, we present the first results of such decomposition, where the quasi-homogeneous sections (QHS) are determined on the basis of the modified bifurcation diagrams, and the units are reconstructed within the limits connected with the problem of shape defects. Nevertheless, the proposed analysis of the local climate dynamics (QHS-analysis) allows to exhibit how the comparatively modest temperature differences between the mentioned units in an annual scale can step-by-step expand into the great temperature differences of the daily variability at a centennial scale. Then the norms and the hazards relate to the fundamentally different viewpoints, where the time sections of months and, especially, seasons distort the causal effects of natural dynamical processes. The specific circumstances to realize the qualitative changes of the local climate dynamics are summarized by the notion of a likely periodicity. That, in particular, allows to explain why [Formula: see text]-year averaging remains the most common rule so far, but the decadal averaging begins to substitute that rule. We believe that the QHS-analysis can be considered as the joint between the norms and the hazards from a bifurcation analysis viewpoint, where the causal effects of the local climate dynamics are projected into the customary timescale only at the last step. We believe that the results could be interesting to develop the fields connected with climatic change and risk assessment.

scholarly journals Offsetting effects of aerosols on Arctic and global climate in the late 20th century

2013 ◽  
Vol 13 (11) ◽  
pp. 30929-30943
Author(s):  
Q. Yang ◽  
C. M. Bitz ◽  
S. J. Doherty
Keyword(s):  
20Th Century ◽  
Air Temperature ◽  
Radiative Forcing ◽  
Global Climate ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Late 20Th Century ◽  
Climate Effect ◽  
Climate System Model ◽  
Arctic Surface

Abstract. We examine the impacts of atmospheric aerosols on Arctic and global climate using a series of 20th century transient simulations from Community Climate System Model version 4 (CCSM4). We focus on the response of surface air temperature to the direct radiative forcing driven by changes in sulfate and black carbon (BC) concentrations from 1975 to 2005 and we also examine the response to sulfate, BC, and organic carbon aerosols varying at once. The direct forcing from sulfate dominates the aerosol climate effect. Globally averaged, all three aerosols produce a cooling trend of 0.015 K decade−1 during the period 1975–2005. In the Arctic, surface air temperature has large spatial variations in response to changes in aerosol concentrations. Over the European Arctic, aerosols induce about 0.6 K decade−1 warming which is about 1.8 K warming over the 30 yr period. This warming is triggered mainly by the reduction in sulfate and BC emissions over Europe since the 1970s and is reinforced by sea ice loss and a strengthening in atmospheric northward heat transport. Over the Siberian and North American Arctic, surface air temperature is likely influenced primarily by changes in aerosol emissions from Asia. An increase in sulfate emissions over Asia induces a large cooling while an increase in BC over Asia causes a significant warming.

scholarly journals An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2012 ◽  
Vol 6 (4) ◽  
pp. 3317-3348 ◽  
Author(s):  
C. Brutel-Vuilmet ◽  
M. Ménégoz ◽  
G. Krinner
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Linear Correlation ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Snow Cover Extent ◽  
Global Mean

Abstract. The 20th century seasonal Northern Hemisphere land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a~reduced spring snow cover extent over the 1979–2005 is underestimated. We show that this is linked to the simulated Northern Hemisphere extratropical land warming trend over the same period, which is underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between hemispheric seasonal spring snow cover extent and boreal large-scale annual mean surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear correlation between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the sensitivity of the Northern Hemisphere spring snow cover to global mean surface air temperature changes is underestimated at present because of the underestimate of the boreal land temperature change amplification.

scholarly journals EC-Earth3-AerChem, a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6

2020 ◽  
Author(s):  
Twan van Noije ◽  
Tommi Bergman ◽  
Philippe Le Sager ◽  
Declan O'Donnell ◽  
Risto Makkonen ◽  
...  
Keyword(s):  
20Th Century ◽  
Southern Hemisphere ◽  
Air Temperature ◽  
Atmospheric Chemistry ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Air Temperature ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract. This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model and describe in detail how it differs from the other EC-Earth3 configurations, and what the new features are compared to the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under pre-industrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The mean energy imbalance at the top of the atmosphere in the pre-industrial control simulation is −0.10 ± 0.25 W m−2 and shows no significant drift. The corresponding mean global surface air temperature is 14.05 ± 0.16 °C, with a small drift of −0.075 ± 0.009 °C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9 °C and its transient climate response at 2.1 °C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread among ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared to the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts, the ensemble mean surface air temperature climatology for 1995–2014 has an average bias of −0.86 ± 0.35 °C in the Northern Hemisphere and 1.29 ± 0.05 °C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant climate effects from the 20th century onwards. For the SSP3-7.0 shared socio-economic pathway, the model gives a global warming at the end of the 21st century (2091–2100) of 4.9 °C above the pre-industrial mean. A 0.5 °C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5 °C.

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