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A model validation approach for echo state networks applied to spatio-temporal climate data
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Global-mean surface temperature is affected by both natural variability and anthropogenic forcing. This study is concerned with identifying and removing from global-mean temperatures the signatures of natural climate variability over the period January 1900-March 2009. A series of simple, physically based methodologies are developed and applied to isolate the climate impacts of three known sources of natural variability: the El Nino-Southern Oscillation (ENSO), variations in the advection of marine air masses over the high-latitude continents during winter, and aerosols injected into the stratosphere by explosive volcanic eruptions. After the effects of ENSO and high-latitude temperature advection are removed from the global-mean temperature record, the signatures of volcanic eruptions and changes in instrumentation become more clearly apparent. After the volcanic eruptions are subsequently filtered from the record, the residual time series reveals a nearly monotonic global warming pattern since similar to 1950. The results also reveal coupling between the land and ocean areas on the interannual time scale that transcends the effects of ENSO and volcanic eruptions. Globally averaged land and ocean temperatures are most strongly correlated when ocean leads land by; 2-3 months. These coupled fluctuations exhibit a complicated spatial signature with largest-amplitude sea surface temperature perturbations over the Atlantic Ocean.
In this study, ultraviolet TOMS (Total Ozone Mapping Spectrometer) satellite data for SO2 are reevaluated for the first 15 days following the 15 June 1991 Pinatubo eruption to reflect new data retrieval and reduction methods. Infrared satellite SO2 data from the TOVS/HIRS/2 (TIROS (Television Infrared Observation Satellite) Optical Vertical Sounder/High Resolution Infrared Radiation Sounder/2) sensor, whose data sets have a higher temporal resolution, are also analyzed for the first time for Pinatubo. Extrapolation of SO2 masses calculated from TOMS and TOVS satellite measurements 19-118 hours after the eruption suggest initial SO2 releases of 15 ± 3 Mt for TOMS and 19 ± 4 Mt for TOVS, including SO2 sequestered by ice in the early Pinatubo cloud. TOVS estimates are higher in part because of the effects of early formed sulfate. The TOMS SO2 method is not sensitive to sulfate, but can be corrected for the existence of this additional emitted sulfur. The mass of early formed sulfate in the Pinatubo cloud can be estimated with infrared remote sensing at about 4 Mt, equivalent to 3 Mt SO2. Thus the total S release by Pinatubo, calculated as SO2, is 18 ± 4 Mt based on TOMS and 19 ± 4 Mt based on TOVS. The SO2 removal from the volcanic cloud during 19-374 hours of atmospheric residence describes overall e-folding times of 25 ± 5 days for TOMS and 23 ± 5 days for TOVS. These removal rates are faster in the first 118 hours after eruption when ice and ash catalyze the reaction, and then slow after heavy ash and ice fallout. SO2 mass increases in the volcanic cloud are observed by both TOMS and TOVS during the first 70 hours after eruption, most probably caused by the gas-phase SO 2 release from sublimating stratospheric ice-ash-gas mixtures. This result suggests that ice-sequestered SO2 exists in all tropical volcanic clouds, and at least partially explains SO2 mass increases observed in other volcanic clouds in the first day or two after eruption.
A benchmark calculation is proposed for evaluating the dynamical cores of atmospheric general circulation models (GCMs) independently of the physical parameterizations. The test focuses on the long-term statistical properties of a fully developed general circulation; thus, it is particularly appropriate for intercomparing the dynamics used in climate models. To illustrate the use of this benchmark, two very different atmospheric dynamical cores--one spectral, one finite difference--are compared. It is found that the long-term statistics produced by the two models are very similar. Selected results from these calculations are presented to initiate the intercomparison.
Long‐lead forecasting for spatio‐temporal systems can entail complex nonlinear dynamics that are difficult to specify a priori. Current statistical methodologies for modeling these processes are often highly parameterized and, thus, challenging to implement from a computational perspective. One potential parsimonious solution to this problem is a method from the dynamical systems and engineering literature referred to as an echo state network (ESN). ESN models use reservoir computing to efficiently compute recurrent neural network forecasts. Moreover, multilevel (deep) hierarchical models have recently been shown to be successful at predicting high‐dimensional complex nonlinear processes, particularly those with multiple spatial and temporal scales of variability (such as those we often find in spatio‐temporal environmental data). Here, we introduce a deep ensemble ESN (D‐EESN) model. Despite the incorporation of a deep structure, the presented model is computationally efficient. We present two versions of this model for spatio‐temporal processes that produce forecasts and associated measures of uncertainty. The first approach utilizes a bootstrap ensemble framework, and the second is developed within a hierarchical Bayesian framework (BD‐EESN). This more general hierarchical Bayesian framework naturally accommodates non‐Gaussian data types and multiple levels of uncertainties. The methodology is first applied to a data set simulated from a novel non‐Gaussian multiscale Lorenz‐96 dynamical system simulation model and, then, to a long‐lead United States (U.S.) soil moisture forecasting application. Across both applications, the proposed methodology improves upon existing methods in terms of both forecast accuracy and quantifying uncertainty.
Stratospheric temperature increases due to Pinatubo aerosols
- K Labitzke
- M P Mccormick
Labitzke, K., & McCormick, M. P. (1992). Stratospheric temperature increases due to Pinatubo aerosols. Geophysical
Research Letters, 19(2), 207-210.
An Idealized Representation of a Stratospheric Aerosol Injection in E3SM (Working Paper)
- Joe Hollowed
Hollowed, Joe. (2023). An Idealized Representation of a Stratospheric Aerosol Injection in E3SM (Working Paper). University of
Michigan.