We report the discovery of a transiting, temperate, Neptune-sized exoplanet orbiting the nearby (d = 27.5 pc), M3V star TOI-1231 (NLTT 24399, L 248-27, 2MASS J10265947-5228099). The planet was detected using photometric data from the Transiting Exoplanet Survey Satellite and followed up with observations from the Las Cumbres Observatory and the Antarctica Search for Transiting ExoPlanets program. Combining the photometric data sets, we find that the newly discovered planet has a radius of 3.65-0.15+0.16R circle plus M (circle plus). With an equilibrium temperature of just 330 K, TOI-1231 b is one of the coolest small planets accessible for atmospheric studies thus far, and its host star's bright near-infrared brightness (J = 8.88, K (s) = 8.07) makes it an exciting target for the Hubble Space Telescope and the James Webb Space Telescope. Future atmospheric observations would enable the first comparative planetology efforts in the 250-350 K temperature regime via comparisons with K2-18 b. Furthermore, TOI-1231's high systemic radial velocity (70.5 km s(-1)) may allow for the detection of low-velocity hydrogen atoms escaping the planet by Doppler, shifting the H i Ly alpha stellar emission away from the geocoronal and interstellar medium absorption features.

HD 21749 is a bright (V = 8.1 mag) K dwarf at 16 pc known to host an inner terrestrial planet HD 21749c as well as an outer sub-Neptune HD 21749b, both delivered by Transiting Exoplanet Survey Satellite (TESS). Follow-up spectroscopic observations measured the mass of HD 21749b to be 22.7 +/- 2.2 M-circle plus with a density of 7.0(-1.3)(+1.6) g cm(-3), making it one of the densest sub-Neptunes. However, the mass measurement was suspected to be influenced by stellar rotation. Here, we present new high-cadence PFS RV data to disentangle the stellar activity signal from the planetary signal. We find that HD 21749 has a similar rotational time-scale as the planet's orbital period, and the amplitude of the planetary orbital RV signal is estimated to be similar to that of the stellar activity signal. We perform Gaussian process regression on the photometry and RVs from HARPS and PFS to model the stellar activity signal. Our new models reveal that HD 21749b has a radius of 2.86 +/- 0.20 R-circle plus, an orbital period of 35.6133 +/- 0.0005 d with a mass of M-b = 20.0 +/- 2.7 M-circle plus and a density of 4.8(-1.4)(+2.0) g cm(-3) on an eccentric orbit with e = 0.16 +/- 0.06, which is consistent with the most recent values published for this system. HD 21749c has an orbital period of 7.7902 +/- 0.0006 d, a radius of 1.13 +/- 0.10 R-circle plus, and a 3 sigma mass upper limit of 3.5 M-circle plus. Our Monte Carlo simulations confirm that without properly taking stellar activity signals into account, the mass measurement of HD 21749b is likely to arrive at a significantly underestimated error bar.

We report the discovery of two short-period massive giant planets from NASA's Transiting Exoplanet Survey Satellite (TESS). Both systems, TOI-558 (TIC 207110080) and TOI-559 (TIC 209459275), were identified from the 30 minute cadence full-frame images and confirmed using ground-based photometric and spectroscopic followup observations from TESS's follow-up observing program working group. We find that TOI-558 b, which transits an F-dwarf (M-* =1.349(-0.065)(+0.064) M-circle dot, R-* =1.496(-0.040)(+0.042) R-circle dot, T-eff = 6466(-93)(+95) K, age 1.79(-0.73)(+0.91) Gyr) with an orbital period of 14.574 days, has a mass of 3.61 +/- 0.15 M-J, a radius of 1.086(-0.038)(+0.041) R-J, and an eccentric (e = 0.300(-0.020)(+0.022)) orbit. TOI-559 b transits a G dwarf (M-* = 1.026 +/- 0.057 M-circle dot, R-* =1.233(-0.026)(+0.028) R-circle dot, T-eff = 5925(-76)(+85) K, age 6.8(-2.0)(+2.5) Gyr) in an eccentric (e = 0.151 +/- 0.011) 6.984 days orbit with a mass of 6.01(-0.23)(+0.24) M-J and a radius of 1.091(-0.025+)(0.028) R-J. Our spectroscopic follow up also reveals a long-term radial velocity trend for TOI-559, indicating a long-period companion. The statistically significant orbital eccentricity measured for each system suggests that these planets migrated to their current location through dynamical interactions. Interestingly, both planets are also massive (>3 M-J), adding to the population of massive giant planets identified by TESS. Prompted by these new detections of high-mass planets, we analyzed the known mass distribution of hot and warm Jupiters but find no significant evidence for multiple populations. TESS should provide a near magnitude-limited sample of transiting hot Jupiters, allowing for future detailed population studies.

Hot Jupiters-short-period giant planets-were the first extrasolar planets to be discovered, but many questions about their origin remain. NASA's Transiting Exoplanet Survey Satellite (TESS), an all-sky search for transiting planets, presents an opportunity to address these questions by constructing a uniform sample of hot Jupiters for demographic study through new detections and unifying the work of previous ground-based transit surveys. As the first results of an effort to build this large sample of planets, we report here the discovery of 10 new hot Jupiters (TOI-2193A b, TOI-2207b, TOI-2236b, TOI-2421b, TOI-2567b, TOI-2570b, TOI-3331b, TOI-3540A b, TOI-3693b, TOI-4137b). All of the planets were identified as planet candidates based on periodic flux dips observed by TESS, and were subsequently confirmed using ground-based time-series photometry, high-angular-resolution imaging, and high-resolution spectroscopy coordinated with the TESS Follow-up Observing Program. The 10 newly discovered planets orbit relatively bright F and G stars (G < 12.5, T (eff) between 4800 and 6200 K). The planets' orbital periods range from 2 to 10 days, and their masses range from 0.2 to 2.2 Jupiter masses. TOI-2421b is notable for being a Saturn-mass planet and TOI-2567b for being a "sub-Saturn," with masses of 0.322 +/- 0.073 and 0.195 +/- 0.030 Jupiter masses, respectively. We also measured a detectably eccentric orbit (e = 0.17 +/- 0.05) for TOI-2207b, a planet on an 8 day orbit, while placing an upper limit of e < 0.052 for TOI-3693b, which has a 9 day orbital period. The 10 planets described here represent an important step toward using TESS to create a large and statistically useful sample of hot Jupiters.

Addressing a variety of questions within Earth science disciplines entails the inference of the spatiotemporal distribution of parameters of interest based on observations of related quantities. Such estimation problems often represent inverse problems that are formulated as linear optimization problems. Computational limitations arise when the number of observations and/or the size of the discretized state space becomes large, especially if the inverse problem is formulated in a probabilistic framework and therefore aims to assess the uncertainty associated with the estimates. This work proposes two approaches to lower the computational costs and memory requirements for large linear space-time inverse problems, taking the Bayesian approach for estimating carbon dioxide (CO2) emissions and uptake (a.k.a. fluxes) as a prototypical example. The first algorithm can be used to efficiently multiply two matrices, as long as one can be expressed as a Kronecker product of two smaller matrices, a condition that is typical when multiplying a sensitivity matrix by a covariance matrix in the solution of inverse problems. The second algorithm can be used to compute a posteriori uncertainties directly at aggregated spatiotemporal scales, which are the scales of most interest in many inverse problems. Both algorithms have significantly lower memory requirements and computational complexity relative to direct computation of the same quantities (O(n(2.5)) vs. O(n(3))). For an examined benchmark problem, the two algorithms yielded massive savings in floating point operations relative to direct computation of the same quantities. Sample computer codes are provided for assessing the computational and memory efficiency of the proposed algorithms for matrices of different dimensions.

Confrontation of climate models with observationally-based reference datasets is widespread and integral to model development. These comparisons yield skill metrics quantifying the mismatch between simulated and reference values and also involve analyst choices, or meta-parameters, in structuring the analysis. Here, we systematically vary five such meta-parameters (reference dataset, spatial resolution, regridding approach, land mask, and time period) in evaluating evapotranspiration (ET) from eight CMIP5 models in a factorial design that yields 68 700 intercomparisons. The results show that while model-data comparisons can provide some feedback on overall model performance, model ranks are ambiguous and inferred model skill and rank are highly sensitive to the choice of meta-parameters for all models. This suggests that model skill and rank are best represented probabilistically rather than as scalar values. For this case study, the choice of reference dataset is found to have a dominant influence on inferred model skill, even larger than the choice of model itself. This is primarily due to large differences between reference datasets, indicating that further work in developing a community-accepted standard ET reference dataset is crucial in order to decrease ambiguity in model skill.

Data assimilation (DA) approaches, including variational and the ensemble Kalman filter methods, provide a computationally efficient framework for solving the CO2 source-sink estimation problem. Unlike DA applications for weather prediction and constituent assimilation, however, the advantages and disadvantages of DA approaches for CO2 flux estimation have not been extensively explored. In this study, we compare and assess estimates from two advanced DA approaches (an ensemble square root filter and a variational technique) using a batch inverse modeling setup as a benchmark, within the context of a simple one-dimensional advection-diffusion prototypical inverse problem that has been designed to capture the nuances of a real CO2 flux estimation problem. Experiments are designed to identify the impact of the observational density, heterogeneity, and uncertainty, as well as operational constraints (i.e., ensemble size, number of descent iterations) on the DA estimates relative to the estimates from a batch inverse modeling scheme. No dynamical model is explicitly specified for the DA approaches to keep the problem setup analogous to a typical real CO2 flux estimation problem. Results demonstrate that the performance of the DA approaches depends on a complex interplay between the measurement network and the operational constraints. Overall, the variational approach (contingent on the availability of an adjoint transport model) more reliably captures the large-scale source-sink patterns. Conversely, the ensemble square root filter provides more realistic uncertainty estimates. Selection of one approach over the other must therefore be guided by the carbon science questions being asked and the operational constraints under which the approaches are being applied.

Terrestrial biosphere models (TBMs) have become an integral tool for extrapolating local observations and understanding of land-atmosphere carbon exchange to larger regions. The North American Carbon Program (NACP) Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) is a formal model intercomparison and evaluation effort focused on improving the diagnosis and attribution of carbon exchange at regional and global scales. MsTMIP builds upon current and past synthesis activities, and has a unique framework designed to isolate, interpret, and inform understanding of how model structural differences impact estimates of carbon uptake and release. Here we provide an overview of the MsTMIP effort and describe how the MsTMIP experimental design enables the assessment and quantification of TBM structural uncertainty. Model structure refers to the types of processes considered (e.g., nutrient cycling, disturbance, lateral transport of carbon), and how these processes are represented (e.g., photosynthetic formulation, temperature sensitivity, respiration) in the models. By prescribing a common experimental protocol with standard spin-up procedures and driver data sets, we isolate any biases and variability in TBM estimates of regional and global carbon budgets resulting from differences in the models themselves (i.e., model structure) and model-specific parameter values. An initial intercomparison of model structural differences is represented using hierarchical cluster diagrams (a.k.a. dendrograms), which highlight similarities and differences in how models account for carbon cycle, vegetation, energy, and nitrogen cycle dynamics. We show that, despite the standardized protocol used to derive initial conditions, models show a high degree of variation for GPP, total living biomass, and total soil carbon, underscoring the influence of differences in model structure and parameterization on model estimates.