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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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Geir Evensen
,
Femke C. Vossepoel
, and
Peter Jan van Leeuwen

Abstract

This paper identifies and explains particular differences and properties of adjoint-free iterative ensemble methods initially developed for parameter estimation in petroleum models. The aim is to demonstrate the methods’ potential for sequential data assimilation in coupled and multiscale unstable dynamical systems. For this study, we have introduced a new nonlinear and coupled multiscale model based on two Kuramoto-Sivashinsky equations operating on different scales where a coupling term relaxes the two model variables towards each other. This model provides a convenient testbed for studying data assimilation in highly nonlinear and coupled multiscale systems. We show that the model coupling leads to cross-covariance between the two models’ variables, allowing for a combined update of both models. The measurements of one model’s variable will also influence the other and contribute to a more consistent estimate. Secondly, the new model allows us to examine the properties of iterative ensemble smoothers and assimilation updates over finite-length assimilation windows. We discuss the impact of varying the assimilation windows’ length relative to the model’s predictability time scale. Furthermore, we show that iterative ensemble smoothers significantly improve the solution’s accuracy compared to the standard ensemble-Kalman-filter update. Results and discussions provide an enhanced understanding of the ensemble methods’ potential implementation and use in operational weather and climate-prediction systems.

Open access
Taylor D. Swaim
,
Emalee Hough
,
Zachary Yap
,
Jamey D. Jacob
,
Siddharth Krishnamoorthy
,
Daniel C. Bowman
,
Léo Martire
,
Attila Komjathy
, and
Brian R. Elbing

Abstract

Heliotropes are passive solar hot air balloons that are capable of achieving nearly level flight within the lower stratosphere for several hours. These inexpensive flight platforms enable stratospheric sensing with high-cadence enabled by the low cost to manufacture, but their performance has not yet been assessed systematically. During July to September of 2021, 29 heliotropes were successfully launched from Oklahoma and achieved float altitude as part of the Balloon-based Acoustic Seismology Study (BASS). All of the heliotrope envelopes were nearly identical with only minor variations to the flight line throughout the campaign. Flight data collected during this campaign comprise a large sample to characterize the typical heliotrope flight behavior during launch, ascent, float, and descent. Each flight stage is characterized, dependence on various parameters is quantified, and a discussion of nominal and anomalous flights is provided.

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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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Satoru Yoshida
,
Tetsu Sakai
,
Tomohiro Nagai
,
Yasutaka Ikuta
,
Teruyuki Kato
,
Koichi Shiraishi
,
Ryohei Kato
, and
Hiromu Seko

Abstract

We conducted field observations using two water vapor Raman lidars (RLs) in Kyushu, Japan, to clarify the characteristics of a moist low-level jet (MLLJ), which plays a fundamental role in the formation and maintenance of mesoscale convective systems (MCSs). The two RLs observed the inside and outside of an MLLJ, providing moisture to an MCS with local heavy precipitation on 9 July 2021. Our observations revealed that the MLLJ contained large amounts of moisture below the convective mixing layer height of 1.6 km. The large amount of the moisture in the MLLJ might be intensified by low-level convergences and/or water vapor buoyancy facilitated by strong horizontal wind. We conducted four data assimilation experiments; CNTL assimilated Japan Meteorological Agency operational observation data, and other three experiments that ingested the lidar-derived vertical moisture profiles as well as the operational observation data. The experiments assimilating lidar-derived vertical moisture profiles caused intensification and southwestward extensions of the low-level convergence zone, resulting in local heavy precipitation at lower latitudes in experiments assimilating lidar-derived moisture profiles than in CNTL. All three experiments ingesting vertical moisture profiles generally produced better nine-hour precipitation forecasts than CNTL, implying that the assimilation of vertical moisture profiles could be well suited for numerical weather prediction of local heavy precipitation. Moreover, experiment assimilating both two RL sites data reproduced better forecast fields than experiments assimilating single RL site data, implying that data assimilation of vertical moisture profiles at multiple RL sites enables us to improve initial conditions compared to single RL site.

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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