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Pei-Syuan Liao
,
Chia-Wei Lan
,
Yu-Chiao Liang
, and
Min-Hui Lo

Abstract

The annual range (AR) of precipitation in the Amazon River basin has increased steadily since 1979. This increase may have resulted from natural variability and/or anthropogenic forcing, such as local land-use changes and global warming, which has yet to be explored. In this study, climate model experiments using the Community Earth System Model version 2 (CESM2) were conducted to examine the relative contributions of sea surface temperatures (SSTs) variability and anthropogenic forcings to the AR changes in the Amazon rainfall. With CESM2, we design several factorial simulations, instead of actual model projection. We found that the North Atlantic SSTs fluctuation dominantly decreases the precipitation AR trend over the Amazon by −85%. In contrast, other factors, including deforestation and carbon dioxide, contribute to the trend changes, ranging from 25∼35%. The dynamic component, specifically the tendency of vertical motion, made negative contributions, along with the vertical profiles of moist static energy (MSE) tendency. Seasonal-dependent changes in atmospheric stability could be associated with variations in precipitation. It is concluded that surface ocean warming associated with the North Atlantic natural variability and global warming is the key factor in the increased precipitation AR over the Amazon from 1979 to 2014. The continuous local land use changes may potentially influence the precipitation AR in the future.

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Lucas R. Vargas Zeppetello
,
Lily N. Zhang
,
David S. Battisti
, and
Marysa M. Laguë

Abstract

Interannual fluctuations in average summertime temperatures across the western United States are captured by a leading EOF that explains over 50% of the total observed variance. In this paper, we explain the origins of this pattern of interannual temperature variability by examining soil moisture-temperature coupling that acts across seasons in observations and climate models. We find that a characteristic pattern of coupled temperature-soil moisture climate variability accounts for 34% of the total observed variance in summertime temperature across the region. This pattern is reproduced in state-of-the-art global climate models, where experiments that eliminate soil moisture variability reduce summertime average temperature variance by a factor of three on average. We use an idealized model of the coupled atmospheric boundary layer and underlying land surface to demonstrate that feedbacks between soil moisture, boundary layer relative humidity, and precipitation can explain the observed relations between springtime soil moisture and summertime temperature. Our results suggest that antecedent soil moisture conditions and subsequent land-atmosphere interactions play an important role in interannual summertime temperature variability in the western U.S.; soil moisture variations cause distal temperature anomalies and impart predictability at timescales longer than one season. Our results indicate that 40% of the observed warming trend across the western U.S. since 1981 has been driven by wintertime precipitation trends in the U.S. southwest.

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Harold E. Brooks
,
Montgomery L. Flora
, and
Michael E. Baldwin

Abstract

Forecast evaluation metrics have been discovered and rediscovered in a variety of contexts, leading to confusion. We look at measures from the 2x2 contingency table and the history of their development and illustrate how different fields working on similar problems has led to different approaches and perspectives of the same mathematical concepts. For example, Probability of Detection is a quantity in meteorology that was also called Prefigurance in the field, while the same thing is named Recall in information science and machine learning, and Sensitivity and True Positive Rate in the medical literature. Many of the scores that combine three elements of the 2x2 table can be seen as either coming from a perspective of Venn diagrams or from the Pythagorean means, possibly weighted, of two ratios of performance measures. Although there are algebraic relationships between the two perspectives, the approaches taken by authors led them in different directions, making it unlikely that they would discover scores that naturally arose from the other approach.

We close by discussing the importance of understanding the implicit or explicit values expressed by the choice of scores. In addition, we make some simple recommendations about the appropriate nomenclature to use when publishing interdisciplinary work.

Open access
J. R. Levey
and
A. Sankarasubramanian

Abstract

Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resources management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash-Sutcliffe Efficiency (NSE) coefficient, has been analyzed towards understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components – correlation, conditional bias, and unconditional bias – by four seasons, three lead times (1–12-day, 1–22 day, and 1–32 day), and three models, European Centre of Medium-Range Weather Forecasts (ECMWF), National Center for Environmental Prediction’s (NCEP) Climate Forecast System (CFS) model, and Environment and Climate Change Canada (ECCC), over the Conterminous United States (CONUS). Application of a dry threshold, removal of grid cells with seasonal climatological precipitation means below 0.01 inches per day, is important as the NSE and correlations are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months and for areas further from the coast. Post-processing using simple Model Output Statistics could reduce both unconditional and conditional bias to zero, thereby offering better skill for regimes with high correlation. Our decomposition results show efforts should focus on improving model parameterization and initialization schemes for climate regimes with low correlation.

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Olawale James Ikuyajolu
,
Luke Van Roekel
,
Steven R Brus
,
Erin E Thomas
,
Yi Deng
, and
James J Benedict

Abstract

This study investigates the sensitivity of the Madden-Julian Oscillation (MJO) to changes to the bulk flux parameterization and the role of ocean surface waves in air-sea coupling using a fully-coupled ocean-atmosphere-wave model. The atmospheric and ocean model components of the Energy Exascale Earth System Model (E3SM) are coupled to a spectral wave model, WAVEWATCH III (WW3). Two experiments with wind speed dependent bulk algorithms, NCAR (Large and Yeager 2004, 2009) & COARE3.0a (Fairall et al. 2003), and one experiment with wave-state dependent flux (COR3.0a-WAV) were conducted. We modify COARE3.0a to include surface roughness calculated within WW3 and also account for the buffering effect of waves on the relative difference between air-side and ocean-side momentum flux. Differences in surface fluxes, primarily caused by discrepancies in drag coefficients, result in significant differences in MJO’s properties. While COARE3.0a has better convection-circulation coupling than NCAR, it exhibits anomalous MJO convection east of the dateline. The wave-state dependent flux (COR3.0-WAV) improves the MJO representation over the default COARE3.0 algorithm. Strong easterlies over the Pacific Ocean in COARE3.0a enhance the latent heat flux (LHFLX). This is responsible for the anomalous MJO propagation after the dateline. In COR3.0a-WAV, waves reduce the anomalous easterlies, leading to a decrease in LHFLX and MJO dissipation after the dateline. These findings highlight the role of surface fluxes in MJO simulation fidelity. Most importantly, we show that the proper treatment of wave-induced effects in bulk flux parameterization improves the simulation of coupled climate variability.

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Suqiong Hu
,
Wenjun Zhang
,
Masahiro Watanabe
,
Feng Jiang
,
Fei-Fei Jin
, and
Han-Ching Chen

Abstract

El Niño-Southern Oscillation (ENSO), the dominant mode of interannual variability in the tropical Pacific, is well known to affect the extratropical climate via atmospheric teleconnections. Extratropical atmospheric variability may in turn influence the occurrence of ENSO events. The winter North Pacific Oscillation (NPO), as the secondary dominant mode of atmospheric variability over the North Pacific, has been recognized as a potential precursor for ENSO development. This study demonstrates that the pre-existing winter NPO signal is primarily excited by sea surface temperature (SST) anomalies in the equatorial western-central Pacific. During ENSO years with a preceding winter NPO signal, which accounts for approximately 60% of ENSO events observed in 1979–2021, significant SST anomalies emerge in the equatorial western-central Pacific in the preceding autumn and winter. The concurrent presence of local convection anomalies can act as a catalyst for NPO-like atmospheric circulation anomalies. In contrast, during other ENSO years, significant SST anomalies are not observed in the equatorial western-central Pacific during the preceding winter, and correspondingly, the NPO signal is absent. Ensemble simulations using an atmospheric general circulation model driven by observed SST anomalies in the tropical western-central Pacific can well reproduce the interannual variability of observed NPO. Therefore, an alternative explanation for the observed NPO-ENSO relationship is that the preceding winter NPO is a companion to ENSO development, driven by the precursory SST signal in the equatorial western-central Pacific. Our results suggest that the lagged relationship between ENSO and the NPO involves a tropical-extratropical two-way coupling rather than a purely stochastic forcing of the extratropical atmosphere on ENSO.

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Peter J. Marinescu
,
Daniel Abdi
,
Kyle Hilburn
,
Isidora Jankov
, and
Liao-Fan Lin

Abstract

Estimates of soil moisture from two National Oceanic and Atmospheric Administration (NOAA) models are compared to in situ observations. The estimates are from a high-resolution atmospheric model with a land surface model [High-Resolution Rapid Refresh (HRRR) model] and a hydrologic model from the NOAA Climate Prediction Center (CPC). Both models produce wetter soils in dry regions and drier soils in wet regions, as compared to the in situ observations. These soil moisture differences occur at most soil depths but are larger at the deeper depths below the surface (100 cm). Comparisons of soil moisture variability are also assessed as a function of soil moisture regime. Both models have lower standard deviations as compared to the in situ observations for all soil moisture regimes. The HRRR model’s soil moisture is better correlated with in situ observations for drier soils as compared to wetter soils—a trend that was not present in the CPC model comparisons. In terms of seasonality, soil moisture comparisons vary depending on the metric, time of year, and soil moisture regime. Therefore, consideration of both the seasonality and soil moisture regime is needed to accurately determine model biases. These NOAA soil moisture estimates are used for a variety of forecasting and societal applications, and understanding their differences provides important context for their applications and can lead to model improvements.

Significance Statement

Soil moisture is an essential variable coupling the land surface to the atmosphere. Accurate estimates of soil moisture are important for forecasting near-surface temperature and moisture, predicting where clouds will form, and assessing drought and fire risks. There are multiple estimates of soil moisture available, and in this study, we compare soil moisture estimates from two different National Oceanic and Atmospheric Administration (NOAA) models to in situ observations. These comparisons include both soil moisture amount and variability and are conducted at several soil depths, in different soil moisture regimes, and for different seasons and years. This comprehensive assessment allows for an accurate assessment of biases within these models that would be missed when conducting analyses more broadly.

Open access
Fu Liu
,
Ralf Toumi
,
Han Zhang
, and
Dake Chen

Abstract

Precipitation plays a crucial role in modulating upper-ocean salinity and the formation of the barrier layer, which affects the development of tropical cyclones (TCs). This study performed idealized simulations to investigate the influence of precipitation on the upper ocean. Precipitation acts to suppress the wind-induced sea surface reduction and generates an asymmetric warming response with a rightward bias. There is substantial vertical change with a cooling anomaly in the subsurface, which is about 3 times larger than the surface warming. The mean tropical cyclone heat potential is locally increased, but the net effect across the cyclone footprint is small. The impact of precipitation on the ocean tends to saturate for extreme precipitation, suggesting a nonlinear feedback. A prevailing driver of the model behavior is that the freshwater flux from precipitation strengthens the stratification and increases current shear in the upper ocean, trapping more kinetic energy in the surface layer and subsequently weakening near-inertial waves in the deep ocean. This study highlights the competing roles of TC precipitation and wind. Because the TC category is weaker than category 3, the warming anomaly is caused by reduced vertical mixing, whereas for stronger storms, the advection process is most important.

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Joshua B. Wadler
,
Joseph J. Cione
,
Samantha Michlowitz
,
Benjamin Jaimes de la Cruz
, and
Lynn K. Shay

Abstract

This study uses fixed buoy time series to create an algorithm for sea surface temperature (SST) cooling underneath a tropical cyclone (TC) inner-core. To build predictive equations, SST cooling is first related to single variable predictors such as the SST before storm arrival, ocean heat content (OHC), mixed layer depth, sea surface salinity and stratification, storm intensity, storm translation speed, and latitude. Of all the single variable predictors, initial SST before storm arrival explains the greatest amount of variance for the change in SST during storm passage. Using a combination of predictors, we created nonlinear predictive equations for SST cooling. In general, the best predictive equations have four predictors and are built with knowledge about the pre-storm ocean structure (e.g., OHC), storm intensity (e.g., minimum sea level pressure), initial SST values before storm arrival, and latitude. The best performing SST cooling equations are broken up into two ocean regimes: when the ocean heat content is less than 60 kJcm−2 (greater spread in SST cooling values) and when the ocean heat content is greater than 60 kJcm−2 (SST cooling is always less than 2 °C) which demonstrates the importance of initial oceanic thermal structure on the in-storm SST value. The new equations are compared to what is currently used in a statistical-dynamical model. Overall, since the ocean providing the latent heat and sensible heat fluxes necessary for TC intensification, the results highlight the importance for consistently obtaining accurate in-storm upper-oceanic thermal structure for accurate TC intensity forecasts.

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Tongya Liu
,
Yu-Kun Qian
,
Xiaohui Liu
,
Shiqiu Peng
, and
Dake Chen

Abstract

Two recently proposed mixing diagnostics are employed to estimate the global surface irreversible mixing based on particle and tracer simulation driven by satellite-derived geostrophic velocities. These two novel diagnostics, similar to the traditional dispersion diffusivity and Nakamura’s effective diffusivity but defined in a localized and instantaneous sense, have the following advantages: 1) they reconcile the theoretical discrepancies between Eulerian-, particle-, and contour-based diffusivities and 2) they do not rely on the stationary and homogeneous assumptions of the turbulent ocean and are free from traditional average operators (e.g., Eulerian time–space or along-contour mean). Our results show that evident discrepancies among these three types of diffusivities do emerge when employing traditional estimates. However, these discrepancies could be significantly mitigated with the adoption of new diagnostic methods, implying that the three types of diffusivities can be effectively reconciled within a global framework. Moreover, finescale mixing structures and transient elevated mixing events due to geostrophic stirring can be clearly identified by the two new diagnostics, in contrast to previous estimates that are spatially and/or temporally smoothed. In particular, it is interesting to note that large values of the new diagnostics usually occur along narrow filaments/fronts associated with mesoscale eddies, and elevated mixing is observed to be located at the periphery of eddies. Our study presents a novel revisit of the global surface mixing induced by geostrophic eddies with an emphasis on irreversibility and provides new insights into previous questions regarding different mixing diagnostics in the community.

Significance Statement

Previous estimates of eddy mixing over the global ocean, using particle-based, tracer-based, and Eulerian-based diffusivities, have shown evident discrepancies. By using recently proposed novel mixing diagnostics, this study demonstrates that the three types of diffusivity estimates agree well with each other, indicating a practical unification of the three types of diffusivities. Also, since the new mixing diagnostics do not involve any traditional average operator, the local and instantaneous mixing maps over the global ocean are presented here, in contrast to previous spatial- or temporal-averaged ones. These new insights can address several unresolved issues in the mixing community.

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