Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

/pdf/climate-change-2007-the-physical-science-basis-1x93ttz9jt.pdf

Climate Change 2007: The Physical Science Basis.

The Tropical Ocean-Global Atmosphere (TOGA) program sought to determine the predictability of the coupled ocean-atmosphere system. The World Climate Research Programme's (WCRP) Global Ocean-Atmosphere-Land System (GOALS) program seeks to explore predictability of the global climate system through investigation of the major planetary heat sources and sinks, and interactions between them. The Asian-Australian monsoon system, which undergoes aperiodic and high amplitude variations on intraseasonal, annual, biennial and interannual timescales is a major focus of GOALS. Empirical seasonal forecasts of the monsoon have been made with moderate success for over 100 years. More recent modeling efforts have not been successful. Even simulation of the mean structure of the Asian monsoon has proven elusive and the observed ENSO-monsoon relationships has been difficult to replicate. Divergence in simulation skill occurs between integrations by different models or between members of ensembles of the same model. This degree of spread is surprising given the relative success of empirical forecast techniques. Two possible explanations are presented: difficulty in modeling the monsoon regions and nonlinear error growth due to regional hydrodynamical instabilities. It is argued that the reconciliation of these explanations is imperative for prediction of the monsoon to be improved. To this end, a thorough description of observed monsoon variability and the physical processes that are thought to be important is presented. Prospects of improving prediction and some strategies that may help achieve improvement are discussed.

/pdf/monsoons-processes-predictability-and-the-prospects-for-1auwiao0qt.pdf

Monsoons: Processes, predictability, and the prospects for prediction

. Aerosols affect the climate system by changing cloud characteristics in many ways. They act as cloud condensation and ice nuclei, they may inhibit freezing and they could have an influence on the hydrological cycle. While the cloud albedo enhancement (Twomey effect) of warm clouds received most attention so far and traditionally is the only indirect aerosol forcing considered in transient climate simulations, here we discuss the multitude of effects. Different approaches how the climatic implications of these aerosol effects can be estimated globally as well as improvements that are needed in global climate models in order to better represent indirect aerosol effects are discussed in this paper.

/pdf/global-indirect-aerosol-effects-a-review-4rvzg5cij1.pdf

Global indirect aerosol effects: a review

This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986-2005, unless otherwise stated.

Chapter 12 - Long-term climate change: Projections, commitments and irreversibility

Description of the NCAR Community Atmosphere Model (CAM 3.0)

[1] The climate responses to the Maunder Minimum-type total solar irradiance (TSI) decrease in the pre-industrial (PI) era and IPCC B1 global warming scenario are examined using the NCAR CAM3 coupled with a mixed-layer slab ocean model. The TSI reduction shifts the AO/NAO state to a more negative index, resulting in regional surface air temperature (SAT) change in the model. In addition, the positive sea ice-solar radiation feedback amplifies the surface cooling due to the TSI decrease. The global annual average cooling effect induced by the TSI decrease is reduced to 0.254°C in the B1 scenario from 0.347°C in the PI epoch, this difference being due to both the suppressed sea ice-solar radiation feedback and the stronger greenhouse effect associated with the increase of greenhouse gases. However, regional SAT changes in the B1 scenario are strengthened markedly due to the enhanced negative AO/NAO. The warming in the western Greenland, Central Asia and East Asia will be enhanced in the B1 global warming scenario when the TSI decreases. These differences in response between the B1 and PI scenarios illustrate that the impact of multi-decadal solar variation on climate depends on the atmospheric background trace gas composition. A future solar grand minimum does not offset the CO2-induced warming in the B1 scenario, but through the enhanced negative AO/NAO may indeed amplify the warming in certain regions.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2009GL041290

Increased greenhouse gases enhance regional climate response to a Maunder Minimum

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020MS002050

Simulated Precipitation Diurnal Variation With a Deep Convective Closure Subject to Shallow Convection in Community Atmosphere Model Version 5 Coupled With CLUBB

With increasing computational capabilities, cumulus parameterizations that are adaptable to the smaller grid spacing and temporal interval for high-resolution climate model simulations are needed. In this study, we propose a method to improve the resolution adaptability of the Zhang-McFarlane (ZM) scheme, by implementing spatial and temporal averaging to the CAPE tendency. This method allows for a more consistent application of the quasi-equilibrium (QE) hypothesis at high spatial and temporal resolutions. The resolution adaptability of the original ZM scheme, the scheme with spatial averaging, and the scheme with spatiotemporal averaging at 4–32 km grid spacings are assessed using the Weather Research and Forecasting (WRF) model by comparing to cloud resolving model (CRM) simulation results coarse-grained to these same grid spacings. We show the original ZM scheme has poor resolution adaptability, with spatiotemporally averaged subgrid convective transport and convective precipitation increasing significantly as the resolution increases. The spatial averaging method improves the resolution adaptability of the ZM scheme and better conserves total transport and total precipitation. Temporal averaging further improves the resolution adaptability of the scheme. With better constrained (although smoothed) convective transport and precipitation, both the spatial distribution and time series of total precipitation at 4 and 8 km grid spacings are improved with the averaging methods. The results could help develop resolution adaptability for other cumulus parameterizations that are based on the QE assumption.

Assessing the Resolution Adaptability of the Zhang‐McFarlane Cumulus Parameterization With Spatial and Temporal Averaging

Abstract. In climate models, subgrid parameterizations of
convection and clouds are one of the main causes of the biases in
precipitation and atmospheric circulation simulations. In recent years, due
to the rapid development of data science, machine learning (ML)
parameterizations for convection and clouds have been demonstrated to have
the potential to perform better than conventional parameterizations. Most
previous studies were conducted on aqua-planet and idealized models, and the
problems of simulation instability and climate drift still exist. Developing
an ML parameterization scheme remains a challenging task in realistically
configured models. In this paper, a set of residual deep neural networks
(ResDNNs) with a strong nonlinear fitting ability is designed to emulate a
super-parameterization (SP) with different outputs in a hybrid ML–physical
general circulation model (GCM). It can sustain stable simulations for over
10 years under real-world geographical boundary conditions. We explore the
relationship between the accuracy and stability by validating multiple deep
neural network (DNN) and ResDNN sets in prognostic runs. In addition, there
are significant differences in the prognostic results of the stable ResDNN
sets. Therefore, trial and error is used to acquire the optimal ResDNN set
for both high skill and long-term stability, which we name the
neural network (NN) parameterization. In offline validation, the neural network parameterization can
emulate the SP in mid- to high-latitude regions with a high accuracy.
However, its prediction skill over tropical ocean areas still needs
improvement. In the multi-year prognostic test, the hybrid ML–physical GCM
simulates the tropical precipitation well over land and significantly
improves the frequency of the precipitation extremes, which are vastly
underestimated in the Community Atmospheric Model version 5 (CAM5), with a
horizontal resolution of 1.9∘ × 2.5∘.
Furthermore, the hybrid ML–physical GCM simulates the robust signal of the
Madden–Julian oscillation with a more reasonable propagation speed than
CAM5. However, there are still substantial biases with the hybrid
ML–physical GCM in the mean states, including the temperature field in the
tropopause and at high latitudes and the precipitation over tropical oceanic
regions, which are larger than those in CAM5. This study is a pioneer in
achieving multi-year stable climate simulations using a hybrid ML–physical
GCM under actual land–ocean boundary conditions that become sustained over
30 times faster than the target SP. It demonstrates the emerging potential
of using ML parameterizations in climate simulations.


Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes

The eastern Pacific double-ITCZ bias has long been attributed to the warm bias of SST in the southeastern Pacific and associated local air–sea interaction. In this study, we conducted two s...

/pdf/culprit-of-the-eastern-pacific-double-itcz-bias-in-the-ncar-1dhdavca86.pdf

Guang J. Zhang

Papers

Increased greenhouse gases enhance regional climate response to a Maunder Minimum

Simulated Precipitation Diurnal Variation With a Deep Convective Closure Subject to Shallow Convection in Community Atmosphere Model Version 5 Coupled With CLUBB

Assessing the Resolution Adaptability of the Zhang‐McFarlane Cumulus Parameterization With Spatial and Temporal Averaging

Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes

Culprit of the Eastern Pacific Double-ITCZ Bias in the NCAR CESM1.2