climate predictability
Recently Published Documents


TOTAL DOCUMENTS

79
(FIVE YEARS 14)

H-INDEX

26
(FIVE YEARS 2)

MAUSAM ◽  
2021 ◽  
Vol 70 (2) ◽  
pp. 357-362
Author(s):  
Narendra Singh ◽  
S. Kimothi ◽  
A. Kumar ◽  
A. Thapliyal ◽  
N. Ojha ◽  
...  

2021 ◽  
pp. 1-55
Author(s):  
Pengfei Shi ◽  
Bin Wang ◽  
Yujun He ◽  
Hui Lu ◽  
Kun Yang ◽  
...  

AbstractLand surface is a potential source of climate predictability over the Northern Hemisphere mid-latitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a DRP-4DVar-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe.


2021 ◽  
Vol 3 ◽  
Author(s):  
Olawale J. Ikuyajolu ◽  
Fabrizio Falasca ◽  
Annalisa Bracco

Global warming is posed to modify the modes of variability that control much of the climate predictability at seasonal to interannual scales. The quantification of changes in climate predictability over any given amount of time, however, remains challenging. Here we build upon recent advances in non-linear dynamical systems theory and introduce the climate community to an information entropy quantifier based on recurrence. The entropy, or complexity of a system is associated with microstates that recur over time in the time-series that define the system, and therefore to its predictability potential. A computationally fast method to evaluate the entropy is applied to the investigation of the information entropy of sea surface temperature in the tropical Pacific and Indian Oceans, focusing on boreal fall. In this season the predictability of the basins is controlled by two regularly varying non-linear oscillations, the El Niño-Southern Oscillation and the Indian Ocean Dipole. We compute and compare the entropy in simulations from the CMIP5 catalog from the historical period and RCP8.5 scenario, and in reanalysis datasets. Discrepancies are found between the models and the reanalysis, and no robust changes in predictability can be identified in future projections. The Indian Ocean and the equatorial Pacific emerge as troublesome areas where the modeled entropy differs the most from that of the reanalysis in many models. A brief investigation of the source of the bias points to a poor representation of the ocean mean state and basins' connectivity at the Indonesian Throughflow.


2021 ◽  
Author(s):  
Annika Drews ◽  
Wenjuan Huo ◽  
Katja Matthes ◽  
Kunihiko Kodera ◽  
Tim Kruschke

Abstract. Despite several studies on decadal-scale solar influence on climate, a systematic detection of solar-induced signals at the surface and the Sun's contribution to decadal climate predictability is still missing. Here, we disentangle the solar-cycle-induced climate response from internal variability and from other external forcings such as greenhouse gases. We utilize two 10-member ensemble simulations with a state-of-the-art chemistry climate model, to date a unique data set in chemistry climate modelling. We quantify the potential predictability related to the solar cycle and demonstrate that the detectability of the solar influence on surface climate depends on the magnitude of the solar cycle. Further, we show that a strong solar cycle forcing organizes and synchronizes the decadal-scale component of the North Atlantic Oscillation, the dominant mode of climate variability in the North Atlantic region.


2021 ◽  
Author(s):  
Balasubramanya Nadiga

<p>  Reduced-order dynamical models play a central role in developing our<br>  understanding of predictability of climate irrespective of whether<br>  we are dealing with the actual climate system or surrogate climate<br>  models. In this context, the Linear Inverse Modeling (LIM) approach,<br>  by helping capture a few essential interactions between dynamical<br>  components of the full system, has proven valuable in being able to<br>  provide insights into the dynamical behavior of the full system.</p><p>  We demonstrate that Reservoir Computing (RC), a form of machine<br>  learning suited for learning in the context of chaotic dynamics,<br>  provides an alternative nonlinear approach that improves on the LIM<br>  approach. We do this in the example setting of predicting sea<br>  surface temperature in the North Atlantic in the pre-industrial<br>  control simulation of a popular earth system model, the Community<br>  Earth System Model version 2 (CESM2) so that we can compare the<br>  performance of the new RC based approach with the traditional LIM<br>  approach both when learning data is plentiful and when such data is<br>  more limited. The useful predictive skill of the RC approach over a<br>  wider range of conditions---larger number of retained EOF<br>  coefficients, extending well into the limited data regime,<br>  etc.---suggests that this machine learning approach may have a use<br>  in climate predictability studies. While the possibility of<br>  developing a climate emulator---the ability to continue the<br>  evolution of the system on the attractor long after failing to be<br>  able to track the reference trajectory---is demonstrated in context<br>  of the Lorenz-63 system, it is suggested that further development of<br>  the RC approach may permit such uses of the new approach in settings<br>  of relevance to realistic predictability studies.</p>


Author(s):  
Laura D. Riihimaki ◽  
Connor Flynn ◽  
Allison McComiskey ◽  
Dan Lubin ◽  
Yann Blanchard ◽  
...  

CapsuleThe maturing of ground-based solar shortwave spectral measurements at the U.S. DOE ARM User Facility facilitates progress in climate predictability by constraining cloud and aerosol radiative effects in complex environments.


Sign in / Sign up

Export Citation Format

Share Document