An Assessment of Climate Feedbacks in Observations and Climate Models Using Different Energy Balance Frameworks

This study evaluates the performance of Coupled Model Intercomparison Project (CMIP) phase 5 and phase 6 models by comparing feedbacks in models to those inferred from observations. Overall, we find no systematic disagreements between the feedbacks in the model ensembles and feedbacks inferred from observations, although there is a wide range in the ability of individual models to reproduce the observations. In particular, 40 of 52 models have best estimates that fall within the uncertainty of the observed total feedback. We quantify two sources of uncertainty in the model ensembles: (1) the structural difference, due to the differences in model parameterizations, and (2) the unforced pattern effect, due to unforced variability, and find that both are important when comparing to an 18-year observational data set. We perform the comparison using two energy balance frameworks: the traditional energy balance framework, in which it is assumed that changes in energy balance are controlled by changes in global average surface temperatures, and an alternative framework that assumes the changes in energy balance are controlled by tropical atmospheric temperatures. We find that the alternative framework provides a more robust way of comparing the models to observations, with both smaller structural differences and smaller unforced pattern effect. However, when considering the relation of feedbacks in response to interannual variability and long-term warming, the traditional framework has advantages. There are no great differences between the CMIP5 and CMIP6 ensembles’ ability to reproduce the observed feedbacks.

Discovering differential genome sequence activity with interpretable and efficient deep learning

Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Pattern Effect, two black-box methods that can interpret genome regulatory sequences for cell type-specific or condition specific patterns. We show that these methods identify relevant transcription factor motifs and spacings that are predictive of cell state-specific chromatin accessibility. Finally, we integrate these methods into framework that is readily accessible to non-experts and available for download as a binary or installed via PyPI or bioconda at https://cgs.csail.mit.edu/deepaccess-package/.

Estimate of equilibrium climate sensitivity and uncertainties by using observations and CMIP6 global coupled climate models

<p>Energy budget estimates of the effective climate sensitivity (effCS) are derived based on estimates of the historical forcing and of observations of the sea surface temperature variations and the ocean heat uptake. Recent revisions to Greenhouse gas forcing and aerosol forcing estimates are included and the data is extended to 2018. We consider two different approaches to derive the effCS from the energy budget: 1) a difference of the energy budget between the recent period 2005-2018 and a base period 1861-1880 (following Sherwood 2020) and 2)  a regression of the differential form of energy budget over the period 1955-2017 (following Gregory et al. 2020). These estimates of the effCS over the historical period are representative of the climate feedback experienced by the climate during the historical period. When accounting for the uncertainty in the forcing, the surface temperature and the ocean heat uptake estimates plus the uncertainty due to the internal variability we find a range of effCS of [1.0;9.7] (at the 95%CL) with a median of 2.0 K with approach (1) and [1.2;2.7] with a median of 1.7 K with approch (2). We find that the lower and the upper tail of the distribution in effCS arise dominantly from the uncertainty in the historical forcing, particularly for the regression method, and at a lower extent for the difference method. This is consistent with previous studies (e.g. Lewis and Curry 2018 and Sherwood et al. 2020).</p><p>Using the same approach based on historical observations but accounting for the pattern effect and the temperature dependence of the feedback estimated with climate model simulations, we derive new estimates of the effECS that should encompass the equilibrium climate sensitivity (assuming that climate model simulate properly the pattern effect and the temperature dependence of feedback). We find that adding the pattern effect and the temperature dependence of the feedbacks shifts upwards the median of the effECS and increases significantly the uncertainty range. For the difference method, the median is now 2.5 K and the uncertainty range [1.1;17.2]. For the regression method the median is now 2.0 K and the uncertainty range is [1.2;4.7] K ((5-95%). On the overall, we find that the regression method performs better to constraint the equilibrium climate sensitivity and that the major source of uncertainties comes from the differences in the simulation of the pattern effect among climate models rather than the uncertainties on the historical forcing.</p>

Negligible Unforced Historical Pattern Effect on Climate Feedback Strength Found in HadISST-Based AMIP Simulations

Recently it has been suggested that natural variability in sea surface temperature (SST) patterns over the historical period causes a low bias in estimates of climate sensitivity based on instrumental records, in addition to that suggested by time variation of the climate feedback parameter in atmospheric general circulation models (GCMs) coupled to dynamic oceans. This excess, unforced, historical “pattern effect” (the effect of evolving surface temperature patterns on climate feedback strength) has been found in simulations performed using GCMs driven by AMIPII SST and sea ice changes (amipPiForcing). Here we show, in both amipPiForcing experiments with one GCM and by using Green’s functions derived from another GCM, that whether such an unforced historical pattern effect is found depends on the underlying SST dataset used. When replacing the usual AMIPII SSTs with those from the HadISST1 dataset in amipPiForcing experiments, with sea ice changes unaltered, the first GCM indicates pattern effects that are indistinguishable from the forced pattern effect of the corresponding coupled GCM. Diagnosis of pattern effects using Green’s functions derived from the second GCM supports this result for five out of six non-AMIPII SST reconstruction datasets. Moreover, internal variability in coupled GCMs is rarely sufficient to account for an unforced historical pattern effect of even one-quarter the strength previously reported. The presented evidence indicates that, if unforced pattern effects have been as small over the historical record as our findings suggest, they are unlikely to significantly bias climate sensitivity estimates that are based on long-term instrumental observations and account for forced pattern effects obtained from GCMs.