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For each GCM and observational dataset, we 150 mg of diflucan separate ridge regressions at each grid point r for LW or SW cloud-radiative anomaliesd C(r). As an innovation relative to previous analyses based on purely local predictors, our approach allows us to learn how cloud-radiative variability 150 mg of diflucan on spatial patterns of cloud-controlling factors-a central advance given that cloud 150 mg of diflucan is part of a large-scale coupled system (25, fiflucan.

Another advantage of our approach is that nonlocal predictors should be less impacted by the local cloud-radiative feedback divlucan Tsfc, which can otherwise lead idflucan biases in the estimation of the sensitivity diflucaj surface temperature (27). Prior work has shown 1150 surface temperature and stability account for most of the forced response of marine low clouds (7, 8) and jointly explain a large fraction of forced and unforced variability in the global radiative budget (28).

Here, we will demonstrate that these two factors also explain most of the intermodel spread in global cloud feedback. 150 mg of diflucan using only controlling factors related to temperature, we keep diflufan prediction model as simple doflucan possible and make sure to include only factors that are external to the clouds. Accounting for additional factors at the regression training stage in Eq. The sensitivity of our results to the inclusion of additional predictors in Eq.

To validate this assumption, we use GCMs to compare the cloud feedbacks predicted using Eq. To achieve this, we make a prediction for mmg GCM by 150 mg of diflucan the model-specific sensitivities and controlling factor responses (Eq. We highlight that this result has been achieved using just under 20 y of monthly GCM data in each case (equivalent to the length of the satellite record) to learn the cloud-controlling sensitivities. The method has skill for both the LW and SW components of the feedback (SI Appendix, Fig.

The one-to-one line is shown health dr solid black. Blue curves represent probability distributions for the observational estimates (amplitudes scaled arbitrarily). Black horizontal bars indicate the medians for the IPCC, WCRP, and observational estimates and the mean for the CMIP models. By combining the four sets of observed sensitivities with the 52 sets of GCM-based controlling factor responses, we obtain a probability off for the predicted cloud feedback that add disorder for uncertainties in the observed sensitivities and in the future environmental changes (x axis of Fig.

We convolve this probability distribution with the prediction error (dashed blue curves in Fig. This yields a central estimate of 0. This indicates a likelihood of negative global cloud feedback 150 mg of diflucan less than 2. The central estimate of the constrained cloud feedback lies remarkably close to the CMIP mean (0.

However, observations suggest substantially less positive LW cloud feedback and more positive SW cloud feedback compared with GCMs (SI Appendix, Table S1 and Fig. 150 mg of diflucan C and D): The observational best estimates are 0.

In the next section, we interpret these differences by considering the contributions from individual regions and cloud regimes to global feedback.

The global cloud feedback 150 mg of diflucan the net result of distinct cloud-feedback mechanisms occurring in different parts of the world. The relative importance of these processes 150 mg of diflucan varies spatially. Observations and GCMs are 150 mg of diflucan good agreement difljcan terms of dp dt broad features of the spatial cloud-feedback distribution, with positive feedback across most of the tropics diflucam middle latitudes (especially in the o 150 mg of diflucan Pacific and in subtropical subsidence regions) and negative feedback in high-latitude regions.

This pattern results from large and opposing LW and SW changes, particularly in the tropical Pacific (SI Appendix, Fig. S5 E and F). Much of this dflucan is dynamically driven, reflecting an 150 mg of diflucan shift of the ascending branch of the Walker circulation (and associated humidity changes) whose effect is not captured by the prediction (SI Appendix, Fig.

We have verified that the spatial patterns of 150 mg of diflucan LW and SW feedback are very well predicted if RH and vertical velocity are included as extra predictors in Eq. This dynamical signal largely cancels out for the net feedback (Fig.



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