A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI‐ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low‐level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two‐layer model.
The Max Planck Institute Grand Ensemble (MPI-GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations and four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO 2 scenario. These advantages make MPI-GE a powerful tool. We present an overview of MPI-GE, its components, and detail the experiments completed. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. We then demonstrate multiple ways to evaluate MPI-GE and put observations in the context of a large ensemble, including a novel approach for comparing model internal variability with estimated observed variability. Finally, we present four novel analyses, which can only be completed using a large ensemble. First, we address whether temperature and precipitation have a pathway dependence using the forcing scenarios. Second, the forced signal of the highly noisy atmospheric circulation is computed, and different drivers are identified to be important for the North Pacific and North Atlantic regions. Third, we use the ensemble dimension to investigate the time dependency of Atlantic Meridional Overturning Circulation variability changes under global warming. Last, sea level pressure is used as an example to demonstrate how MPI-GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects.Large-ensemble projects of comprehensive coupled climate models are gaining traction as methods to robustly estimate internal variability in transient simulations and to quantify the forced signal (e.g., Kay
There is emerging evidence of the important role of indigenous knowledge for climate change adaptation. The necessity to consider different knowledge systems in climate change research has been established in the fifth assessment report (AR5) of the Intergovernmental Panel on Climate Change (IPCC). However, gaps in author expertise and inconsistent assessment by the IPCC lead to a regionally heterogeneous and thematically generic coverage of the topic. We conducted a scoping review of peer-reviewed academic literature to support better integration of the existing and emerging research on indigenous knowledge in IPCC assessments. The research question underpinning this scoping review is: How is evidence of indigenous knowledge on climate change adaptation geographically and thematically distributed in the peer-reviewed academic literature? As the first systematic global evidence map of indigenous knowledge in the climate adaptation literature, the study provides an overview of the evidence of indigenous knowledge for adaptation across regions and categorises relevant concepts related to indigenous knowledge and their contexts in the climate change literature across disciplines. The results show knowledge clusters around tropical rural areas, subtropics, drylands, and adaptation through planning and practice and behavioural measures. Knowledge gaps include research in northern and central Africa, northern Asia, South America, Australia, urban areas, and adaptation through capacity building, as well as institutional and psychological adaptation. This review supports the assessment of indigenous knowledge in the IPCC AR6 and also provides a basis for follow-up research, e.g. bibliometric analysis, primary research of underrepresented regions, and review of grey literature.
During the first decade of the twenty-first century, the Earth’s surface warmed more slowly than climate models simulated1. This surface-warming hiatus is attributed by some studies to model errors in external forcing2, 3, 4, while others point to heat rearrangements in the ocean5, 6, 7, 8, 9, 10 caused by internal variability, the timing of which cannot be predicted by the models1. However, observational analyses disagree about which ocean region is responsible11, 12, 13, 14, 15, 16. Here we show that the hiatus could also have been caused by internal variability in the top-of-atmosphere energy imbalance. Energy budgeting for the ocean surface layer over a 100-member historical ensemble reveals that hiatuses are caused by energy-flux deviations as small as 0.08 W m−2, which can originate at the top of the atmosphere, in the ocean, or both. Budgeting with existing observations cannot constrain the origin of the recent hiatus, because the uncertainty in observations dwarfs the small flux deviations that could cause a hiatus. The sensitivity of these flux deviations to the observational dataset and to energy budget choices helps explain why previous studies conflict, and suggests that the origin of the recent hiatus may never be identified
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