Previous studies have investigated the role of the Pacific meridional mode (PMM), a climate mode of the mid-latitudes in the Northern and Southern Hemisphere, in favoring the development of the El Niño Southern Oscillation (ENSO). However little is known on how ENSO can influence the development of the PMM. Here we investigate the relationship between ENSO and the South Pacific Meridional Mode (SPMM) focusing on strong SPMM events that follows strong El Niño events. This type of events represents more than 60% of such events in the observational record and the historical simulations of the CESM Large ensemble (CESM-LE). It is first shown that such a relationship is rather stationary in both observations and the CESM-LE. Our analyses further reveal that strong SPMM events are associated with a coastal warming off northern central Chile peaking in Austral winter resulting from the propagation of waves forced at the equator during the development of El Niño events. The time delay between the ENSO peak (Boreal winter) and this coastal warming (Austral winter) can be understood in terms of the differential contribution of the equatorially-forced propagating baroclinic waves to the warming along the coast. In particular, the difference in phase speeds of the waves (the high-order mode the wave the slower) implies that they do not overlap along their propagation south of 20°S. This contributes to the persistence of warm coastal SST anomalies off Central Chile until the Austral summer following the concurrent El Niño event. This coastal warming is favorable to the development of strong SPMM events as the South Pacific Oscillation become active during that season. The analysis of the simulations of the Coupled Intercomparison Project phases 5 and 6 (CMIP5/6) indicates that very few models realistically simulate this ENSO/SPMM relationship and associated oceanic teleconnection.

Dewitte, B., E. Concha, D. Sepúlveda, O. Pizarro, C. Martinez-Villalobos, D. Gushchina, M. Ramos, and A. Montecinos: The ENSO-induced South Pacific Meridional Mode. Front. Clim. In press. https://doi.org/10.3389/fclim.2022.1080978

Figure 3. (A) Regression map of the SVD leading mode SST/Wind expansion coefficients for the SST and 10-m wind vectors. The expansion coefficients have been normalized so that units are °C and m/s for SST and wind respectively. The longest arrow on the maps corresponds to a wind amplitude of 0.89 m/s. The covariance of the mode is 83 ± 3.4%. The contour in thick blue line corresponds to the iso-contour 0.25°C of the E mode pattern. (B) Ensemble mean lagged correlation between the SPMM index and (red) E and (blue) C indices. The shading in corresponding color indicates ± the standard deviation amongst the ensemble, while the thin lines indicate the 10% and 90% percentiles. Negative lag means SPMM ahead E/C. (C, D) Distribution of the correlation between the SPMM index and the (C) E and (D) C indices as a function of lead time for 20-year periods chunks of the historical runs of the CESM model. The black lines indicate the 10 and 90% percentiles. The dashed line in blue corresponds to the ensemble mean of the lagged correlation [i.e., curves of (B)]. The red diamonds indicate where the “dip test” for multimodality yields a p value lower than 0.05 (i.e., passing the 95% level).

The performance of GCMs in simulating daily precipitation probability distributions is investigated by comparing 35 CMIP6 models against observational datasets (TRMM-3B42 and GPCP). In these observational datasets, PDFs on wet days follow a power-law range for low and moderate intensities below a characteristic precipitation cutoff scale. Beyond the cutoff scale, the probability drops much faster, hence controlling the size of extremes in a given climate. In the satellite products analyzed, PDFs have no interior peak. Contributions to the first and second moments tend to be single-peaked, implying a single dominant precipitation scale; the relationship to the cutoff scale and log-precipitation coordinate and normalization of frequency density are outlined. Key metrics investigated include the fraction of wet days, PDF power-law exponent, cutoff scale, shape of probability distributions, and number of probability peaks. The simulated power-law exponent and cutoff scale generally fall within observational bounds, although these bounds are large; GPCP systematically displays a smaller exponent and cutoff scale than TRMM-3B42. Most models simulate a more complex PDF shape than these observational datasets, with both PDFs and contributions exhibiting additional peaks in many regions. In most of these instances, one peak can be attributed to large-scale precipitation and the other to convective precipitation. Similar to previous CMIP phases, most models also rain too often and too lightly. These differences in wet-day fraction and PDF shape occur primarily over oceans and may relate to deterministic scales in precipitation parameterizations. It is argued that stochastic parameterizations may contribute to simplifying simulated distributions.

Martinez-Villalobos, C., Neelin, J. D., & Pendergrass, A. G. (2022). Metrics for Evaluating CMIP6 Representation of Daily Precipitation Probability Distributions, Journal of Climate, 35(17), 5719-5743 https://doi.org/10.1175/JCLI-D-21-0617.1

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

Leung, L. R., Boos, W. R., Catto, J. L., A. DeMott, C., Martin, G. M., Neelin, J. D., O’Brien, T. A., Xie, S., Feng, Z., Klingaman, N. P., Kuo, Y., Lee, R. W., Martinez-Villalobos, C., Vishnu, S., Priestley, M. D. K., Tao, C., & Zhou, Y. (2022). Exploratory Precipitation Metrics: Spatiotemporal Characteristics, Process-Oriented, and Phenomena-Based, Journal of Climate, 35(12), 3659-3686 https://doi.org/10.1175/JCLI-D-21-0590.1

Two catastrophic extreme precipitation events in July 2021 and August 1975 caused tremendous
damages and deaths in Henan, one of the most populated provinces in China. Revealing the relationship
between large-scale circulation patterns and precipitation extremes is vital for understanding the physical mechanisms and providing potential value for improving prediction and hence reducing impacts. Here, nine large-scale circulation patterns are identified for July–August using the self-organizing map. We find daily precipitation extremes under the fifth pattern (P5), characterized with the strongest easterly wind anomalies in Henan, feature the highest frequency and the largest intensity. Seven out of total 11 days in the two catastrophic extreme precipitation events belong to P5, and the top two maximum hourly precipitation extremes over continental China occurred under P5. The larger intensity of precipitation extremes is attributed to the dynamical contribution, suggesting more-intense precipitation extremes under P5 are largely dominated by stronger ascending motions.

Zhang, S., Y. Chen, Y. Luo, B. Liu, G. Ren, T. Zhou, C. Martinez-Villalobos, and M. Chang, (2022): Revealing the circulation pattern most conducive to precipitation extremes in Henan Province of North China. Geophysical Research Letters, 49, e2022GL098034. https://doi.org/10.1029/2022GL098034

Purpose of Review:

Review our current understanding of how precipitation is related to its thermodynamic environment, i.e., the water vapor and temperature in the surroundings, and implications for changes in extremes in a warmer climate.

Recent Findings:

Multiple research threads have i) sought empirical relationships that govern onset of strong convective precipitation, or that might identify how precipitation extremes scale with changes in temperature; ii) examined how such extremes change with water vapor in global and regional climate models under warming scenarios; iii) identified fundamental processes that set the characteristic shapes of precipitation distributions.

Summary:

While water vapor increases tend to be governed by the Clausius-Clapeyron relationship to temperature, precipitation extreme changes are more complex and can increase more rapidly, particularly in the tropics. Progress may be aided by bringing separate research threads together and by casting theory in terms of a full explanation of the precipitation probability distribution.

Neelin, J.D., Martinez-Villalobos, C., Stechmann, S.N. et al. Precipitation Extremes and Water Vapor. Curr Clim Change Rep 8, 17–33 (2022). https://doi.org/10.1007/s40641-021-00177-z

This study investigates future changes in daily precipitation extremes and the involved physics over the global land monsoon (GM) region using climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). The daily precipitation extreme is identified by the cutoff scale, measuring the extreme tail of the precipitation distribution. Compared to the historical period, multimodel results reveal a continuous increase in precipitation extremes under four scenarios, with a progressively higher fraction of precipitation exceeding the historical cutoff scale when moving into the future. The rise of the cutoff scale by the end of the century is reduced by 57.8% in the moderate emission scenario relative to the highest scenario, underscoring the social benefit in reducing emissions. The cutoff scale sensitivity, defined by the increasing rates of the cutoff scale over the GM region to the global mean surface temperature increase, is nearly independent of the projected periods and emission scenarios, roughly 8.0% K⁻¹ by averaging all periods and scenarios. To understand the cause of the changes, we applied a physical scaling diagnostic to decompose them into thermodynamic and dynamic contributions. We find that thermodynamics and dynamics have comparable contributions to the intensified precipitation extremes in the GM region. Changes in thermodynamic scaling contribute to a spatially uniform increase pattern, while changes in dynamic scaling dominate the regional differences in the increased precipitation extremes. Furthermore, the large intermodel spread of the projection is primarily attributed to variations of dynamic scaling among models.

Chang M., B. Liu, B. Wang, C. Martinez-Villalobos, G. Ren, and T. Zhou, 2022: Understanding future increases in precipitation extremes in global land monsoon regions. J. Climate, 35(6), 1839-1851, https://doi.org/10.1175/JCLI-D-21-0409.1

Quantitative simulation of precipitation in current climate has been an ongoing challenge for global climate models. Despite serious biases in correctly simulating probabilities of extreme rainfall events, model simulations under global warming scenarios are routinely used to provide estimates of future changes in these probabilities. To minimize the impact of model biases, past literature tends to evaluate fractional (instead of absolute) changes in probabilities of precipitation extremes under the assumption that fractional changes would be more reliable. However, formal tests for the validity of this assumption have been lacking. Here we evaluate two measures that address properties important to the correct simulation of future fractional probability changes of precipitation extremes, and that can be assessed with current climate data. The first measure tests climate model performance in simulating the characteristic shape of the probability of occurrence of daily precipitation extremes and the second measure tests whether the key parameter governing the scaling of this shape is well reproduced across regions and seasons in current climate. Contrary to concerns regarding the reliability of global models for extreme precipitation assessment, our results show most models lying within the current range of observational uncertainty in these measures. Thus, most models in the Coupled Model Intercomparison Project Phase 6 ensemble pass two key tests in current climate that support the usefulness of fractional measures to evaluate future changes in the probability of precipitation extremes.

Cristian Martinez-Villalobos and J David Neelin 2021 Environ. Res. Lett. 16 024017, https://doi.org/10.1088/1748-9326/abd351

Figure 1. (a) Probability distributions for the region shown in figure 2(a) calculated from four observational datasets (GPCP, PERSIANN V1, CMORPH v1 and TRMM-3B42; squares) and one CMIP6 model (GFDL-CM4, black circles). Note the different cutoff scales P_L in each dataset (big circle markers). (b) As in (a) but rescaled by their respective P_L , allowing similarities in the shape of the extreme tail (above ) to be evaluated. Big circle markers shows the location of the cutoff-scale in P coordinates ( in all cases). In addition to datasets in (a), rescaled distributions in ACCESS-ESM1-5, CanESM5, CESM2, FGOALS-g3, INM-CM5-0 and IPSL-CM6A-LR are also shown. (c) Schematic of the effect of an increase in the cutoff scale  on the extreme tail probability. This is illustrated in rescaled coordinates but same fractional changes apply for daily precipitation before rescaling. As an example, we use a fractional increase in  of 21%, which (under no changes in dynamical contribution) would correspond to a Clausius–Clapeyron scaling for a 3 °C temperature increase. Using same τ_P  = 0.5 for both curves yields a fractional increase of the 99.9th daily precipitation percentile  (over wet days) also of 21%. The actual fractional increase of  (or P_99.9) will have a small dependence on how τ_P adjusts (see supplementary material).

Precipitation accumulations, integrated over rainfall events, are investigated using hourly data across continental China during the warm season (May–October) from 1980 to 2015. Physically, the probability of precipitation accumulations drops slowly with event size up to an approximately exponential cutoff scale s_L where probability drops much faster. Hence s_L can be used as an indicator of high accumulation percentiles (i.e., extreme precipitation accumulations). Overall, the climatology of s_L over continental China is about 54 mm. In terms of cutoff changes, the current warming stage (1980–2015) is divided into two periods, 1980–97 and 1998–2015. We find that the cutoff in 1998–2015 increases about 5.6% compared with that of 1980–97, with an average station increase of 4.7%. Regionally, s_L increases are observed over East China (10.9% ± 1.5%), Northwest China (9.7% ± 2.5%), South China (9.4% ± 1.4%), southern Southwest China (5.6% ± 1.2%), and Central China (5.3% ± 1.0%), with decreases over North China (−10.3% ± 1.3%), Northeast China (−4.9% ± 1.5%), and northern Southwest China (−3.9% ± 1.8%). The conditional risk ratios for five subregions with increased cutoff s_L are all greater than 1.0, indicating an increased risk of large precipitation accumulations in the most recent period. For high precipitation accumulations larger than the 99th percentile of accumulation s₉₉, the risk of extreme precipitation over these regions can increase above 20% except for South China. These increases of extreme accumulations can be largely explained by the extended duration of extreme accumulation events, especially for “extremely extreme” precipitation greater than s₉₉.

Chang, M., B. Liu, C. Martinez-Villalobos, G. Ren, S. Li, and T. Zhou, 2020: Changes in Extreme Precipitation Accumulations during the Warm Season over Continental China. J. Climate, 33, 10799–10811, https://doi.org/10.1175/JCLI-D-20-0616.1

The probability distribution of daily precipitation intensities, especially the probability of extremes, impacts a wide range of applications. In most regions this distribution decays slowly with size at first, approximately as a power law with an exponent between 0 and −1, and then more sharply, for values larger than a characteristic cutoff scale. This cutoff is important because it limits the probability of extreme daily precipitation occurrences in current climate. There is a long history of representing daily precipitation using a gamma distribution—here we present theory for how daily precipitation distributions get their shape. Processes shaping daily precipitation distributions can be separated into nonprecipitating and precipitating regime effects, the former partially controlling how many times in a day it rains, and the latter set by single-storm accumulations. Using previously developed theory for precipitation accumulation distributions—which follow a sharper power-law range (exponent < −1) with a physically derived cutoff for large sizes—analytical expressions for daily precipitation distribution power-law exponent and cutoff are calculated as a function of key physical parameters. Precipitating and nonprecipitating regime processes both contribute to reducing the power-law range exponent for the daily precipitation distribution relative to the fundamental exponent set by accumulations. The daily precipitation distribution cutoff is set by the precipitating regime and scales with moisture availability, with important consequences for future distribution shifts under global warming. Similar results extend to different averaging periods, providing insight into how the precipitation intensity distribution evolves as a function of both underlying physical climate conditions and averaging time.

Martinez-Villalobos, C. and J.D. Neelin, 2019: Why Do Precipitation Intensities Tend to Follow Gamma Distributions?. J. Atmos. Sci., 76, 3611–3631, https://doi.org/10.1175/JAS-D-18-0343.1

https://journals.ametsoc.org/doi/full/10.1175/JAS-D-18-0343.1

 

 

 

Previous studies indicate an asymmetry in the amplitude and persistence of El Niño (EN) and La Niña (LN) events. We show that this observed EN‐LN asymmetry can be captured with a linear model driven by correlated additive and multiplicative (CAM) noise, without resorting to a deterministic nonlinear model. The model is derived from 1‐month lag statistics taken from monthly sea surface temperature (SST) data sets spanning the twentieth century, in an extension of an empirical‐dynamical technique called Linear Inverse Modeling. Our results suggest that noise amplitudes tend to be stronger for EN compared to LN events, which is sufficient to generate asymmetry in amplitude and also produces more persistent LN events on average. These results establish a null hypothesis for EN‐LN asymmetry and suggest that strong EN events may not be more predictable that what can be accounted for by a multivariate linear system driven by CAM noise.

C., Martinez‐Villalobos, Newman, M., Vimont, D. J., Penland, C., & Neelin, J. D. ( 2019). Observed El Niño‐La Niña asymmetry in a linear model. Geophysical Research Letters, 46, 9909-9919. https://doi.org/10.1029/2019GL082922

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2019GL082922