The semiarid region between the North African Savanna and Sahara Desert, known as the Sahel, experienced dramatic multidecadal precipitation variability in the 20th century that was unparalleled in the rest of the world, including devastating droughts and famine in the early 1970s and 80s. Accurate predictions of this region’s hydroclimate future are essential to avoid future disasters of this kind, yet simulations from state of the art general circulation models (GCMs) do a poor job of simulating past Sahel rainfall variability, and don’t even agree on whether future precipitation will increase or decrease under global warming. Furthermore, climate scientists are still not in agreement about whether anthropogenic emissions played an important role relative to natural variability in dictating past Sahel rainfall change.
Because the climate system is complex and coupled, it is difficult to determine which processes should be considered causal drivers of circulation changes and which should be considered part of the climate response, and therefore many theories for monsoon rainfall variability coexist in the literature. It is difficult to evaluate these competing theories because observational studies generally cannot be interpreted causally, but simulated experiments may not represent the dynamics of the real world. The Coupled Model Intercomparison Project (CMIP) provides a wealth of data in which GCMs maintained at research institutions worldwide perform similar experiments, allowing the researcher to reach conclusions that are robust to differences in parameterization between GCMs. The scientific community has been using a wide range of statistical techniques to analyze this data, and each has notable limitations. This dissertation explores two statistical techniques for leveraging CMIP to explore the drivers and mechanisms of historical Sahel rainfall variability: analysis of ensemble-mean responses to prescribed variables, and causal inference.
In Chapter 1, we give an overview of the climatology and variability of Sahel rainfall and present relevant physical theory.
In Chapter 2, we examine the roles of various types of anthropogenic forcing in observations and coupled simulations, using a 3-tiered multi-model mean (MMM) to extract robust climate signals from CMIP phase 5 (CMIP5). We examine “20th century” historical and single-forcing simulations—which separate the influence of anthropogenic aerosols, greenhouse gases (GHG), and natural radiative forcing on global coupled ocean-atmosphere system, and were specifically designed for attribution studies—as well as pre-Industrial control simulations, which only contain unforced internal climate variability, to investigate the drivers of simulated Sahel precipitation variability. The comparison of single-forcing and historical simulations highlights the importance of anthropogenic and volcanic aerosols over GHG in generating forced Sahel rainfall variability that reinforces the observed pattern, with anthropogenic aerosols alone responsible for the low-frequency component of simulated variability. However, the forced MMM only accounts for a small fraction of observed variance. A residual consistency test shows that simulated internal variability cannot explain the residual observed multidecadal variability, and points to model deficiency in simulating multidecadal variability in the forced response, internal variability, or both.
In Chapter 3, we investigate the causes for discrepancies in low-frequency Sahel precipitation variability between these ensembles and for model deficiency in reproducing observations. In the most recent version of CMIP – phase 6 of the Coupled Model Intercomparison Project (CMIP6) – the differences between observed and simulated variability are amplified rather than reduced: CMIP6 still grossly underestimates the magnitude of low-frequency variability in Sahel rainfall, but unlike CMIP5, historical mean precipitation in CMIP6 does not even correlate with observed multi-decadal variability. We continue to use a MMM to extract robust climate signals from simulations, but now additionally include sea surface temperature (SST) as a mediating variable in order to test the proposed physical processes. This partitions all influences on Sahel precipitation variability into five components: (1) teleconnections to SST; (2) atmospheric and (3) oceanic variability internal to the climate system; (4) the SST response to external radiative forcing; and (5) the “fast” (not mediated by SST) precipitation response to forcing.
Though the coupled simulations perform quite poorly, in a vast improvement from previous ensembles, the CMIP6 atmosphere-only ensemble is able to reproduce the full magnitude of observed low-frequency Sahel precipitation variance when observed SST is prescribed. The high performance is due entirely to the atmospheric response to observed global SST – the fast response to forcing has a relatively small impact on Sahel rainfall, and only lowers the performance of the ensemble when it is included. Using the previously-established North Atlantic Relative Index (NARI) to approximate the role of global SST, we estimate that the strength of simulated teleconnections is consistent with observations. Applying the lessons of the atmosphere-only ensemble to coupled settings, we infer that both coupled CMIP ensembles fail to explain low-frequency historical Sahel rainfall variability mostly because they cannot explain the observed combination of forced and internal variability in SST. Though the fast response is small relative to the simulated response to observed SST variability, it is influential relative to simulated SST variability, and differences between CMIP5 and CMIP6 in the simulation of Sahel precipitation and its correlation with observations can be traced to differences in the simulated fast response to forcing or the role of other unexamined SST patterns.
In this chapter, we use NARI to approximate the role of global SST because it is considered by some to be the best single index for estimating teleconnections to the Sahel. However, we show that NARI is only able to explain half of the high-performing simulated low-frequency Sahel precipitation variability in the atmospheric simulations with prescribed global SST. Statistical techniques commonly applied in the literature cannot distinguish between correlation and causality, so we cannot analyze the response of Sahel rainfall to global SST in more depth without atmospheric CMIP simulations targeted at every ocean basin of interest or a new method.
In Chapter 4, we turn to a novel technique called causal inference to qualify the notion that NARI can adequately represent the role of global SST in determining Sahel rainfall. We apply a causal discovery algorithm to CMIP6 pre-Industrial control simulations to determine which ocean basins influence Sahel rainfall in individual GCMs. Though we find that state of the art causal discovery algorithms for time series still struggle with data that isn’t generated specifically for algorithm evaluation, we robustly find that NARI does not mediate the full effect of global SST variability on Sahel rainfall in any of the climate simulations. This chapter lays the foundation for future work to fully-characterize the dependence of Sahel precipitation on individual ocean basins using the non-targeted simulations already available in CMIP – an approach which can be validated by comparing the composite results to the interventional historical simulations that are available. Furthermore, we hope this chapter will guide algorithm improvement efforts that are needed to increase the performance and usefulness of time series causal discovery algorithms on climate data.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/8akg-4v69 |
| Date | January 2023 |
| Creators | Herman, Rebecca Jean |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
Page generated in 0.0033 seconds