1,264 research outputs found
Phylloicus elektoros Prather 2003
No full textPhylloicus elektoros Prather 2003 (Fig 21) — Prather 2003: 48 [Type locality: Venezuela, Amazonas, Cerro de la Neblina, Basecamp, in rain forest, 00°51’N, 66°10’W, 140 m a.s.l., NMNH, ♂; ♀. — Martins et al. 2014: 337 [biology]. — Paprocki & França 2014: 5 [checklist]. Material examined. BRAZIL, Amazonas, Manaus, Reserva Ducke, igarapé Barro Branco, 0255’46.70”S 5958’22.00”W, 71 m a.s.l., 16.vi.2015, J.O. da Silva leg., 3♂ (alcohol). Presidente Figueiredo, Balneário Sossego da Pantera, igarapé da Onça, 02º00’52”S 60º01’43”W, 05.v.2000, A.M.O. Pes leg., 5♂ (alcohol). Rio Preto da Eva, PDBFF [Programa de Dinâmica Biológica de Fragmentos], Acampamento do Km 41, 0226’24”S 5946’29”W, 110 m a.s.l., 23.iv.2007, V. L. Landeiro leg., 1♀ (alcohol); Escola Adventista Agroindustrial, 02º41’41.3”S 59º44’06.05”W, 13.iv.2008, C.A.S. Azevedo, M. Pepinelli, U.G. Neiss leg., 1♂ (alcohol). Pará, Belterra, igarapé Branco, 03°03’02.60”S, 054°58’09.30”W, 108 m a.s.l., 19.ix.2016, A.M.O. Pes, J. da Silva, G. Amora, G.D. Gomes leg., Malaise trap, 3♂ and 28♀ (alcohol), Pennsylvania light trap, 3♂ (alcohol); igarapé Jatuarana, 03°15’44.70”S, 054°57’22.00”W, 97 m a.s.l., 24.ix.2016, A.M.O. Pes, J. da Silva, G. Amora, G.D. Gomes leg., Malaise trap, 4♀ (alcohol); igarapé Porto Velho, 03°25’59.10”S, 054°54’59.60”W, 114 m a.s.l., 20.ix.2016, A.M.O. Pes, J. da Silva, G. Amora, G.D. Gomes leg., Pennsylvania light trap, 3♂ (alcohol). Placas, igarapé da Onça, 03°33’48.20”S, 054°52’30.90”W, 77 m a.s.l., 22.ix.2016, A.M.O. Pes, J. da Silva, G. Amora, G.D. Gomes leg., Malaise trap, 4♂ (alcohol), Pennsylvania ligth trap, 1♀ (alcohol). Mojuí dos Campos, Ramal do Km 53 PA-370 Fazenda de guaraná 7 km, 02º48’49.6”S, 054º23’38.2”W, 74 m a.s.l., 12–15.xii.2014, J.O. da Silva, S. R. M. Couceiro leg., Malaise trap, 1♂ (alcohol). Santarém, Ramal Santa Rosa, igarapé Ipixuna, 02º36’59.9”S, 054º26’54.1”W, 24 m a.s.l., 09.xii.2014, J.O. da Silva. S. R. M. Couceiro leg., Malaise trap, 1♀ (alcohol). Distribution: Brazil (AM, PA new record) (Fig 21), Peru, Venezuela.Published as part of Souza-Holanda, Paula Mayara De, Pes, Ana Maria & Hamada, Neusa, 2020, Immature stages of three species and new records of five species of Phylloicus Müller (Trichoptera, Calamoceratidae) in the northern region of Brazil, pp. 111-136 in Zootaxa 4851 (1) on page 119, DOI: 10.11646/zootaxa.4851.1.4, http://zenodo.org/record/440725
FIGURES 1–3. Trichoptera life history stages. 1 in Order Trichoptera Kirby, 1813 (Insecta), Caddisflies *
No full textFIGURES 1–3. Trichoptera life history stages. 1–larva, Halesochila taylori (Limnephilidae); 2–pupa, Ceraclea sp. (Leptoceridae); 3–adult, Hesperophylax designatus (Limnephilidae).Published as part of Holzenthal, Ralph W., Blahnik, Roger J., Prather, Aysha L. & Kjer, Karl M., 2007, Order Trichoptera Kirby, 1813 (Insecta), Caddisflies *, pp. 639-698 in Zootaxa 1668 on page 641, DOI: 10.5281/zenodo.18015
FIGURES 15–18. Trichoptera adults. 15 in Order Trichoptera Kirby, 1813 (Insecta), Caddisflies *
No full textFIGURES 15–18. Trichoptera adults. 15.–Amazonatolica hamadae, head and thorax, lateral (Leptoceridae); 16–Culoptila thoracica, head and thorax, with modified tegulae, dorsal (Glossosomatidae); Tagalopsyche kjaerandseni, head and thorax, dorsal (Leptoceridae); 18–Tolhuaca cupulifera, head and thorax, dorsal (Glossosomatidae). Setose wart (= s.w.) terminology after Ivanov (1990).Published as part of Holzenthal, Ralph W., Blahnik, Roger J., Prather, Aysha L. & Kjer, Karl M., 2007, Order Trichoptera Kirby, 1813 (Insecta), Caddisflies *, pp. 639-698 in Zootaxa 1668 on page 650, DOI: 10.5281/zenodo.18015
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Indirect long-term global radiative cooling from NOx emissions
No full textAnthropogenic emissions of short‐lived, chemically reactive gases, such as NO x and CO, are known to influence climate by altering the chemistry of the global troposphere and thereby the abundance of the greenhouse gases O3, CH4 and the HFCs. This study uses the characteristics of the natural modes of the tropospheric chemical system to decompose the greenhouse effect of NO x and CO emissions into (i) short‐lived modes involving predominantly tropospheric O3 and (ii) the long‐lived mode involving a global coupled CH4‐CO‐O3 perturbation. Combining these two classes of greenhouse perturbations—large, short‐lived, regional O3 increases and smaller, long‐lived, global decreases in CH4 and O3—we find that most types of anthropogenic NO x emissions lead to a negative radiative forcing and an overall cooling of the earth
FIGURES 53–56 in Order Trichoptera Kirby, 1813 (Insecta), Caddisflies *
No full textFIGURES 53–56. Phylogenetic relationships of Trichoptera suborders, alternative hypotheses. 53–hypothesis of Ross; 54–hypothesis of Weaver; 55–hypothesis of Wiggins and Wichard; 56–hypothesis of Ivanov. See text for explanation.Published as part of Holzenthal, Ralph W., Blahnik, Roger J., Prather, Aysha L. & Kjer, Karl M., 2007, Order Trichoptera Kirby, 1813 (Insecta), Caddisflies *, pp. 639-698 in Zootaxa 1668 on page 660, DOI: 10.5281/zenodo.18015
Phylloicus blahniki Prather 2003, new species
No full textPhylloicus blahniki, new species Figs. 28, 29 Phylloicus blahniki is distinguished by the modifications of abdominal segments III and IV of the male. Tergite III is flanged anterolaterally; tergite IV bears a truncate posterior process and the lateral sclerite is short and rounded apically. Only lateral coremata are present, and they consist of four lobes, three of them short and rounded, the posterior lobe slightly elongate (Fig. 28F). The few specimens on which this description is based were all teneral or rubbed, so the description of the wing pattern is incomplete. None of the male specimens had the phallic endotheca fully everted, so I am unable to describe the membranous lobes. Adult. Forewing length 11.7 mm, n = 8. Head golden brown. Maxillary palps golden brown. Antenna twice forewing length; dark brown, with narrow patches of pale sensilla on anteromesal surface of each flagellomere. Dorsal pterothorax golden brown, anterolateral margins dark brown; ventrolateral thorax golden brown. Femora golden brown; tibiae golden brown; tarsi dark brown. Metathoracic leg of male without posterior fringe. Tibial spur formula 2,4,4. Forewing flat; chestnut brown; with longitudinal stripes; stripes pale tan. Hind wing basal brush present in male and female. Male. Preterminalic abdominal terga with anteromesal notch. Corematic structures present. Tergum III with anterolateral flanges and short posterolateral projections. Tergum IV with paired posterior processes and paired lateral sclerites, lateral coremata; posterior process truncate; lateral sclerite narrowed apically; lateral coremata with basal globose lobes. Tergum V without sclerotized modifications (Fig. 28F). Sternum VII with short, acute anteromesal process. Sternum VIII similar to anterior sterna, sternum IX not elongate. Tergum IX without mesal ridge; posterior margin smoothly rounded; thinly sclerotized anteromesally (Fig. 28B); lateral ridge present; dorsal pleural setae absent, ventral pleural setae approximately 10 (Fig. 28A); sternum IX with faint mesolateral ridges; sternum IX (Fig. 28C). Preanal appendage longer than tergum X, but less than 11/2 times length, of uniform diameter throughout length, setae long, but not filamentous or longer than appendage (Fig. 28A, B). Tergum X without basal lobes; basodorsal process absent; basolateral processes absent; apex, in lateral view, acute, in dorsal view, notched, notch triangular; base of tergum X setose; tapered apically (Fig. 28A, B). Harpago slightly tapered; peglike setae few, apical (Fig. 28A, C). Phallotremal sclerites average size, longest dimension less than diameter of phallobase; dorsal sclerite ovoid, in dorsal view horseshoeshaped (Fig. 28D, E). Female. Preterminal abdominal terga with anteromesal notch. Sternum VII with short pointed anteromesal process. Tergum VIII without posterolateral brush; sternum VIII cleft posteromesally to anterior ridge; sternum VIII (Fig. 29C). Tergum IX without mesal ridge (Fig. 29B). Sternum IX anterior and posterior lobes darkly sclerotized and striate, with patch of lightly sclerotized cuticle lateral to vaginal opening (Fig. 29A). Tergum X appendage longer than mesal lobe, base indistinct, apex rounded; mesal lobe lightly sclerotized; digitate lateral processes long, at least twice diameter and often asymmetrical (Fig. 29B). Sternum X with patches of short fine setae posterolaterally to anal opening (Fig. 29A). Vaginal apparatus anterior and posterior sclerites equal in length; anterior sclerite rounded anteriorly, posterolateral projections acute; posterior sclerite triangular (Fig. 29A). Holotype male: COSTA RICA: Puntarenas: Parque Nacional Corcovado, unnamed stream, Piedra el Arco, 08°34'55”N, 83°42'32"W, 20 m, 10.iv.1989, Holzenthal & Blahnik (UMSP). Paratypes: COSTA RICA: Puntarenas: Quebrada Pita, ca. 3 km (air) W Golfito, 08°38'31”N, 83°11'35"W, 15 m, 15.ii.1986, Holzenthal, Morse, & Fasth — 1 male (UMSP); Corcovado National Park, Osa Peninsula, 1522.iii.1979, Janzen — 2 males (INBIO); Parque Nacional Corcovado, Rio Camaronal, 08°28'55”N, 83°35'20"E, 30 m, 13.iv.1989, Holzenthal & Blahnik — 1 female, 2 males (UMSP); PANAMA: Panama: Canal Zone, Barro Colorado Island, SnyderMolino trail, marker 3, 28.ix.4.x.1988, Wolda — 1 male (NMNH). Distribution. Costa Rica, Panama. Etymology. This species is named for Roger J. Blahnik, who collected the type specimen.Published as part of PRATHER, AYSHA L., 2003, Revision of the Neotropical caddisfly genus Phylloicus (Trichoptera: Calamoceratidae), pp. 1-214 in Zootaxa 275 (1) on pages 35-36, DOI: 10.11646/zootaxa.275.1.1, http://zenodo.org/record/501923
Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres
No full text<p>In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.</p><p>Funding provided by: National Aeronautics and Space Administration<br>Crossref Funder Registry ID: https://ror.org/027ka1x80<br>Award Number: 80NSSC21K1454</p><p>Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: https://ror.org/021nxhr62<br>Award Number: AGS-2135749</p><p>Funding provided by: NOAA Climate Program Office<br>Crossref Funder Registry ID: https://ror.org/00mmmy130<br>Award Number: NA22OAR4310476</p><div>A unique and useful advantage of Cloud-ICA is that with low computational cost it readily generates a complete set of ICAs such that the sum of wtICAs equals one. Cloud-ICA can then calculate a set of up-to-four quadrature column atmospheres (QCAs) that can be used to approximate the integral over the full set of ICAs. The method of parsing partially overlapping clouds within a single column atmosphere (SCA) into a large number of ICAs and then into a set of only four QCAs originated in Neu, Prather & Penner (2007) and was fully developed with observation-based overlap models in the chemistry photolysis code Cloud-J v7.3c in Prather (2015). Cloud-J has continued to develop (Prather & Hsu, 2019; 2019d; Hsu & Prather, 2021) and the current Cloud-ICA code is taken from Cloud-J v8.0c (Prather, 2023). </div>
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<div>The primary Cloud-ICA references are:</div>
<div> </div>
<div>Neu, J.L., M.J. Prather, J.E. Penner (2007) Global atmospheric chemistry: integrating over fractional cloud cover, J. Geophys. Res., 112, D11306, doi:10.1029/2006JD008007</div>
<div> </div>
<div>Prather, M.J. (2015) Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c, Geosci. Model Dev., 8, 2587-2595, doi:10.5194/gmd-8-2587-2015</div>
<div> </div>
<div>Prather, M.J. and J.C. Hsu (2019) A round Earth for climate models, Proc Natl Acad Sci, 116 (39) 19330-19335; https://doi.org/10.1073/pnas.1908198116.</div>
<div> </div>
<div>Prather, Michael and Hsu, Juno (2019d), Solar-J and Cloud-J models version 7.6c, v2, UC Irvine Dash, Dataset, doi.org/10.7280/D1096P</div>
<div> </div>
<div>Hsu, J.C. and M.J. Prather (2021), Assessing uncertainties and approximations in solar heating of the climate system. Journal of Advances in Modeling Earth Systems, 13, e2020MS002131. doi: 10.1029/2020MS002131</div>
<div> </div>
<div>Prather, Michael (2023d). An updated cloud-overlap photolysis module for atmospheric chemistry models, UCI Cloud-J v8.0, with near-UV H2O absorption [Dataset]. Dryad. https://doi.org/10.7280/D1Q398</div>
<div> </div>
<div>A description of the cloud overlap algorithm and it basis in observations is take directly from Prather (2015), see references within.</div>
<div> </div>
<div>"Our recommended cloud-overlap model uses the information on vertical correlations (Pincus et al., 2005; Naud and DelGenio, 2006; Kato et al., 2010; Oreopoulis et al., 2012), which shows cloud decorrelation lengths on the order of 1.5 km in the lower atmosphere increasing to 3 km or more in the upper troposphere. Since a true COR model scales as 2**NL and becomes rapidly impractical for high-resolution models, we define vertical groups of cloud layers globally according to the decorrelation lengths: 0–1.5 km altitude, 1.5–3.5, 3.5–6, 6–9, 9–13, and >13 km. We assume that the cloud layers within a decorrelation length are highly correlated with one another and thus form a MAX group. When such MAX groups are adjacent they have a mean separation of one decorrelation length, and we choose a cloud correlation factor of cc=0.33, similar to 1 e-fold. When there is a clear-sky gap between a pair of G6 layers, the MAX groups are separated by more than one decorrelation length; thus, we reduce the factor cc with successive multiples (i.e., with two missing G6 MAX groups between two cloudy layers,the effective cc=0.333**2 =0.036). This model is denoted G6/.33. Two other G6 models were tested: cc=0.00 corresponds to randomly overlapped adjacent groups (MAXRAN, G6/.00); and cc=0.99 is almost maximally overlapped (MAX, G6/.99).</div>
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<div>In looking at how this model aligned the clouds for realistic FCAs, we found that extensive cirrus fractions in the uppermost layers prevented the expected overlap of small fraction cumulus below. Thus, a seventh MAX group is added if there was a cirrus shield (defined from top down as adjacent ice-only clouds with f >0.5). Because of the cloud fraction binning into 10% intervals, the number of ICAs is bounded by 5x106 (including the cirrus shield). This limit is resolution independent and was never reached in any FCAs examined here (highest number of ICAs for one FCA was 3500). The major computational cost comes with the Fast-J computation, and the methods for approximating the average of J values over all ICAs (Sect. 3) use at most four Fast-J calculations no matter how many ICAs. Two other cloud-overlap models tested here are the MAXRAN groupings G0 and G3 (Feng et al., 2004; Neu et al., 2007). Model G0 assumes that all vertically adjacent cloudy layers are a MAX group (maximally overlapped), and all such groups separated by a clear layer are RAN overlapped. This model seems logical but has difficulty finding a clear layer when the FCA has been averaged over several hours or taken from a parameterized cloud-resolving model. It our tests, using meteorological data with NLD36, the maximum number of G0 ICAs was 375. Model G3 has at most three MAX-RAN groups demarcated by atmospheric regimes: a fixed altitude (1.5 km, stratus top) and temperature (the liquid-to-ice cloud transition). The maximum possible number of ICAs per FCA for G3 is 103, and in our tests we found 288.</div>
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<div>Our recommended cloud-overlap model is G6/.33 since it is based on the observed–modeled cloud decorrelation lengths. For a given FCA, we treat the J values calculated by summing Fast-J over all the ICAs generated by G6/.33 as the correct value. We calculate errors for the other cloud-overlap models (here) or various ICA-approximation models using the G6/.33 model (Sect. 3).</div>
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<div>We use a high-resolution snapshot from the European Center for Medium-rangeWeather Forecasts, similar to what is used (at lower resolution) in the UC Irvine and University of Oslo chemistry-transport models (Søvde et al., 2012; Hsu and Prather, 2014). The 640 FCAs are a 3 h average of a single longitudinal belt just above the Equator (T319L60 Cycle 36) and have clouds only in the lowermost 36 layers. Profiles of temperature and ozone are taken from tropical mean observations; the Rayleigh-scattering optical depth at 600 nm is about 0.12, and a mix of aerosol layers has a total optical depth of 0.23. J value errors are calculated separately for each FCA and then averaged. The number of ICAs per FCA averages 169 for model G6, 21 for model G3, and 19 for model G0; see Fig. 2 for the probability distribution of ICA numbers.</div>
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<div>Further discussion about the deterministic cloud cover generator is found in Hsu and Prather (2021)</div>
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<div>"In a manner similar to Hogan and Bozzo's (2018) deterministic cloud-cover generator that goes from MAXRAN to EXP-RAN, Cloud-J developed a deterministic ICA generator for MAX-RAN and then adapted it to use vertical decorrelation lengths in its MAX-COR algorithm (M. J. Prather, 2015). Chemistry models need the selection of ICAs for any overlap method to be deterministic because many critical applications require perturbation-control pairs without stochastic noise (e.g., M. J. Prather & Hsu, 2010). Thus Solar-J cannot use a stochastic cloud generator (e.g., Räisänen et al., 2004), and this drove the structure of our cloud overlap algorithm. MAX-COR was designed to be (i) deterministic, (ii) linear in cost with increasing numbers of layers, and (iii) robust when cloud data are averaged in time or space, because such averaging tends to eliminate cloud-free layers and revert to MAX overlap. Based on observations of decorrelation length (Kato et al., 2010; Naud et al., 2008; Oreopoulos et al., 2012; Pincus et al., 2005), MAX-COR defines 6-layer groupings by altitude range. Because decorrelation is small across the vertical range of each group, we assume MAX overlap within each group and a decorrelation of the overlap of each MAX group with its neighbor. Adopting terminology of climate community, MAX-COR is effectively a MAX-EXP algorithm. By quantizing the cloud fraction to the nearest 10% and allowing an independent cirrus shield at the top, the absolute maximum number of ICAs under MAX-EXP is <5 × 106 and thus ICAs can be rapidly defined and binned with low computational overhead. Deterministic EXP-EXP or EXP-RAN models in our code would have to enumerate up to 233 ICAs for our model that has potentially 33 cloudy layers, which is truly prohibitive and not linearly scalable with resolution. We believe that a MAX-COR or MAX-EXP algorithm is likely the most stable and scalable deterministic ICA generator for vertical cloud decorrelation algorithms. The RRTMG v4.0 code available at the time of this study uses primarily MAX-RAN cloud overlap, but the new v5.0 code includes an EXP-RAN option. Thus, our comparisons of cloud-overlap results with the RRTMG code are limited to MAX-RAN. Within Solar-J we can run both MAX-RAN (SJ/RAN) and the standard MAX-COR (SJ) and thus compare with J. K. P. Shonk and Hogan (2010), as discussed below.</div>
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<div>Let us accept that ICAs generated by cloud overlap algorithms can be solved with 1D RT as horizontally homogeneous plane parallel layers, then the next step is how to solve the RT problem for all ICAs and average the results. The number of ICAs are often numerous enough that no practical climate RT code can solve them all, and most codes do not even count them all (Räisänen et al., 2004). RRTMG randomly selects an ICA for each wavelength bin in the RT solution, a method designated Monte Carlo ICA McICA, Pincus et al., 2003). McICA has errors at each time step by mixing ICAs across wavelengths and by not accurately sampling the average of ICAs (e.g., average cloud optical depth) in that time step. McICA is intended to deliver the correct mean when averaged enough times over the same cloud system, but it has hourly grid-cell rms errors of 40 W m−2 (H. W. Barker et al., 2008; Pincus et al., 2003). A key underlying premise is that solar heating errors propagate symmetrically and linearly in the climate system and average out, as was found for simple forecast models. Assessing net bias errors caused by noisy heating rates would need to examine nonlinear processes in hydrology, cloud systems, ecosystem productivity, and air quality in Earth system models (e.g., Pincus & Stevens, 2013).</div>
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<div>With a deterministic ICA generator, we can calculate an "exact" non-stochastic answer as was done for limited test cases in M. J. Prather (2015), but we could not afford to do this for our January climate metric. Solar-J identifies and sorts all ICAs by cloud optical depth and then selects up to four representative quadrature column atmospheres (QCAs) each with a fractional area to represent the distribution of ICAs. The full-wavelength RT solutions are completed for each QCA (Neu et al., 2007). See Figure S3 for a global picture of the average frequency of occurrence of the 4 QCA bins for January 2015. Cloud quadrature does a very good job of averaging over the ICAs with net bias errors of ∼1% in solar intensity and rms errors of 2%–4%. To reach equivalent accuracy for a single time step using random selection would require about 50 ICAs each with full wavelength calculation (not as in McICA) versus an average of 2.8 QCAs (many grid cells have less than 4 Q</div>
Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres
No full text<p>In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.</p><p>Funding provided by: National Aeronautics and Space Administration<br>Crossref Funder Registry ID: https://ror.org/027ka1x80<br>Award Number: 80NSSC21K1454</p><p>Funding provided by: National Science Foundation<br>Crossref Funder Registry ID: https://ror.org/021nxhr62<br>Award Number: AGS-2135749</p><p>Funding provided by: NOAA Climate Program Office<br>Crossref Funder Registry ID: https://ror.org/00mmmy130<br>Award Number: NA22OAR4310476</p><div>A unique and useful advantage of Cloud-ICA is that with low computational cost it readily generates a complete set of ICAs such that the sum of wtICAs equals one. Cloud-ICA can then calculate a set of up-to-four quadrature column atmospheres (QCAs) that can be used to approximate the integral over the full set of ICAs. The method of parsing partially overlapping clouds within a single column atmosphere (SCA) into a large number of ICAs and then into a set of only four QCAs originated in Neu, Prather & Penner (2007) and was fully developed with observation-based overlap models in the chemistry photolysis code Cloud-J v7.3c in Prather (2015). Cloud-J has continued to develop (Prather & Hsu, 2019; 2019d; Hsu & Prather, 2021) and the current Cloud-ICA code is taken from Cloud-J v8.0c (Prather, 2023). </div>
<div> </div>
<div>The primary Cloud-ICA references are:</div>
<div> </div>
<div>Neu, J.L., M.J. Prather, J.E. Penner (2007) Global atmospheric chemistry: integrating over fractional cloud cover, J. Geophys. Res., 112, D11306, doi:10.1029/2006JD008007</div>
<div> </div>
<div>Prather, M.J. (2015) Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c, Geosci. Model Dev., 8, 2587-2595, doi:10.5194/gmd-8-2587-2015</div>
<div> </div>
<div>Prather, M.J. and J.C. Hsu (2019) A round Earth for climate models, Proc Natl Acad Sci, 116 (39) 19330-19335; https://doi.org/10.1073/pnas.1908198116.</div>
<div> </div>
<div>Prather, Michael and Hsu, Juno (2019d), Solar-J and Cloud-J models version 7.6c, v2, UC Irvine Dash, Dataset, doi.org/10.7280/D1096P</div>
<div> </div>
<div>Hsu, J.C. and M.J. Prather (2021), Assessing uncertainties and approximations in solar heating of the climate system. Journal of Advances in Modeling Earth Systems, 13, e2020MS002131. doi: 10.1029/2020MS002131</div>
<div> </div>
<div>Prather, Michael (2023d). An updated cloud-overlap photolysis module for atmospheric chemistry models, UCI Cloud-J v8.0, with near-UV H2O absorption [Dataset]. Dryad. https://doi.org/10.7280/D1Q398</div>
<div> </div>
<div>A description of the cloud overlap algorithm and it basis in observations is take directly from Prather (2015), see references within.</div>
<div> </div>
<div>"Our recommended cloud-overlap model uses the information on vertical correlations (Pincus et al., 2005; Naud and DelGenio, 2006; Kato et al., 2010; Oreopoulis et al., 2012), which shows cloud decorrelation lengths on the order of 1.5 km in the lower atmosphere increasing to 3 km or more in the upper troposphere. Since a true COR model scales as 2**NL and becomes rapidly impractical for high-resolution models, we define vertical groups of cloud layers globally according to the decorrelation lengths: 0–1.5 km altitude, 1.5–3.5, 3.5–6, 6–9, 9–13, and >13 km. We assume that the cloud layers within a decorrelation length are highly correlated with one another and thus form a MAX group. When such MAX groups are adjacent they have a mean separation of one decorrelation length, and we choose a cloud correlation factor of cc=0.33, similar to 1 e-fold. When there is a clear-sky gap between a pair of G6 layers, the MAX groups are separated by more than one decorrelation length; thus, we reduce the factor cc with successive multiples (i.e., with two missing G6 MAX groups between two cloudy layers,the effective cc=0.333**2 =0.036). This model is denoted G6/.33. Two other G6 models were tested: cc=0.00 corresponds to randomly overlapped adjacent groups (MAXRAN, G6/.00); and cc=0.99 is almost maximally overlapped (MAX, G6/.99).</div>
<div> </div>
<div>In looking at how this model aligned the clouds for realistic FCAs, we found that extensive cirrus fractions in the uppermost layers prevented the expected overlap of small fraction cumulus below. Thus, a seventh MAX group is added if there was a cirrus shield (defined from top down as adjacent ice-only clouds with f >0.5). Because of the cloud fraction binning into 10% intervals, the number of ICAs is bounded by 5x106 (including the cirrus shield). This limit is resolution independent and was never reached in any FCAs examined here (highest number of ICAs for one FCA was 3500). The major computational cost comes with the Fast-J computation, and the methods for approximating the average of J values over all ICAs (Sect. 3) use at most four Fast-J calculations no matter how many ICAs. Two other cloud-overlap models tested here are the MAXRAN groupings G0 and G3 (Feng et al., 2004; Neu et al., 2007). Model G0 assumes that all vertically adjacent cloudy layers are a MAX group (maximally overlapped), and all such groups separated by a clear layer are RAN overlapped. This model seems logical but has difficulty finding a clear layer when the FCA has been averaged over several hours or taken from a parameterized cloud-resolving model. It our tests, using meteorological data with NLD36, the maximum number of G0 ICAs was 375. Model G3 has at most three MAX-RAN groups demarcated by atmospheric regimes: a fixed altitude (1.5 km, stratus top) and temperature (the liquid-to-ice cloud transition). The maximum possible number of ICAs per FCA for G3 is 103, and in our tests we found 288.</div>
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<div>Our recommended cloud-overlap model is G6/.33 since it is based on the observed–modeled cloud decorrelation lengths. For a given FCA, we treat the J values calculated by summing Fast-J over all the ICAs generated by G6/.33 as the correct value. We calculate errors for the other cloud-overlap models (here) or various ICA-approximation models using the G6/.33 model (Sect. 3).</div>
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<div>We use a high-resolution snapshot from the European Center for Medium-rangeWeather Forecasts, similar to what is used (at lower resolution) in the UC Irvine and University of Oslo chemistry-transport models (Søvde et al., 2012; Hsu and Prather, 2014). The 640 FCAs are a 3 h average of a single longitudinal belt just above the Equator (T319L60 Cycle 36) and have clouds only in the lowermost 36 layers. Profiles of temperature and ozone are taken from tropical mean observations; the Rayleigh-scattering optical depth at 600 nm is about 0.12, and a mix of aerosol layers has a total optical depth of 0.23. J value errors are calculated separately for each FCA and then averaged. The number of ICAs per FCA averages 169 for model G6, 21 for model G3, and 19 for model G0; see Fig. 2 for the probability distribution of ICA numbers.</div>
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<div>Further discussion about the deterministic cloud cover generator is found in Hsu and Prather (2021)</div>
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<div>"In a manner similar to Hogan and Bozzo's (2018) deterministic cloud-cover generator that goes from MAXRAN to EXP-RAN, Cloud-J developed a deterministic ICA generator for MAX-RAN and then adapted it to use vertical decorrelation lengths in its MAX-COR algorithm (M. J. Prather, 2015). Chemistry models need the selection of ICAs for any overlap method to be deterministic because many critical applications require perturbation-control pairs without stochastic noise (e.g., M. J. Prather & Hsu, 2010). Thus Solar-J cannot use a stochastic cloud generator (e.g., Räisänen et al., 2004), and this drove the structure of our cloud overlap algorithm. MAX-COR was designed to be (i) deterministic, (ii) linear in cost with increasing numbers of layers, and (iii) robust when cloud data are averaged in time or space, because such averaging tends to eliminate cloud-free layers and revert to MAX overlap. Based on observations of decorrelation length (Kato et al., 2010; Naud et al., 2008; Oreopoulos et al., 2012; Pincus et al., 2005), MAX-COR defines 6-layer groupings by altitude range. Because decorrelation is small across the vertical range of each group, we assume MAX overlap within each group and a decorrelation of the overlap of each MAX group with its neighbor. Adopting terminology of climate community, MAX-COR is effectively a MAX-EXP algorithm. By quantizing the cloud fraction to the nearest 10% and allowing an independent cirrus shield at the top, the absolute maximum number of ICAs under MAX-EXP is <5 × 106 and thus ICAs can be rapidly defined and binned with low computational overhead. Deterministic EXP-EXP or EXP-RAN models in our code would have to enumerate up to 233 ICAs for our model that has potentially 33 cloudy layers, which is truly prohibitive and not linearly scalable with resolution. We believe that a MAX-COR or MAX-EXP algorithm is likely the most stable and scalable deterministic ICA generator for vertical cloud decorrelation algorithms. The RRTMG v4.0 code available at the time of this study uses primarily MAX-RAN cloud overlap, but the new v5.0 code includes an EXP-RAN option. Thus, our comparisons of cloud-overlap results with the RRTMG code are limited to MAX-RAN. Within Solar-J we can run both MAX-RAN (SJ/RAN) and the standard MAX-COR (SJ) and thus compare with J. K. P. Shonk and Hogan (2010), as discussed below.</div>
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<div>Let us accept that ICAs generated by cloud overlap algorithms can be solved with 1D RT as horizontally homogeneous plane parallel layers, then the next step is how to solve the RT problem for all ICAs and average the results. The number of ICAs are often numerous enough that no practical climate RT code can solve them all, and most codes do not even count them all (Räisänen et al., 2004). RRTMG randomly selects an ICA for each wavelength bin in the RT solution, a method designated Monte Carlo ICA McICA, Pincus et al., 2003). McICA has errors at each time step by mixing ICAs across wavelengths and by not accurately sampling the average of ICAs (e.g., average cloud optical depth) in that time step. McICA is intended to deliver the correct mean when averaged enough times over the same cloud system, but it has hourly grid-cell rms errors of 40 W m−2 (H. W. Barker et al., 2008; Pincus et al., 2003). A key underlying premise is that solar heating errors propagate symmetrically and linearly in the climate system and average out, as was found for simple forecast models. Assessing net bias errors caused by noisy heating rates would need to examine nonlinear processes in hydrology, cloud systems, ecosystem productivity, and air quality in Earth system models (e.g., Pincus & Stevens, 2013).</div>
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<div>With a deterministic ICA generator, we can calculate an "exact" non-stochastic answer as was done for limited test cases in M. J. Prather (2015), but we could not afford to do this for our January climate metric. Solar-J identifies and sorts all ICAs by cloud optical depth and then selects up to four representative quadrature column atmospheres (QCAs) each with a fractional area to represent the distribution of ICAs. The full-wavelength RT solutions are completed for each QCA (Neu et al., 2007). See Figure S3 for a global picture of the average frequency of occurrence of the 4 QCA bins for January 2015. Cloud quadrature does a very good job of averaging over the ICAs with net bias errors of ∼1% in solar intensity and rms errors of 2%–4%. To reach equivalent accuracy for a single time step using random selection would require about 50 ICAs each with full wavelength calculation (not as in McICA) versus an average of 2.8 QCAs (many grid cells have less than 4 Q</div>
Photolysis rates in correlated overlapping cloud fields: Cloud-J 7.3c
No full textA new approach for modeling photolysis rates (J values) in atmospheres with
fractional cloud cover has been developed and is implemented as Cloud-J – a
multi-scattering eight-stream radiative transfer model for solar radiation
based on Fast-J. Using observations of the vertical correlation of cloud
layers, Cloud-J 7.3c provides a practical and accurate method for modeling
atmospheric chemistry. The combination of the new maximum-correlated cloud
groups with the integration over all cloud combinations by four quadrature
atmospheres produces mean J values in an atmospheric column with
root mean square (rms) errors of 4 % or less compared with 10–20 % errors
using simpler approximations. Cloud-J is practical for chemistry–climate
models, requiring only an average of 2.8 Fast-J calls per atmosphere vs.
hundreds of calls with the correlated cloud groups, or 1 call with the
simplest cloud approximations. Another improvement in modeling J values,
the treatment of volatile organic compounds with pressure-dependent cross
sections, is also incorporated into Cloud-J
Gridded MDA8 surface ozone observations for the EU and US during 1993-2014
No full text<p>Maximum daily eight-hour average (MDA8) surface ozone derived from gridded hourly observational datasets for the European and US domain [Schnell et al., 2014; 2015; Schnell and Prather, 2017], according to the MDA8 nomenclature of the European Union [EUR-LEX, 2008]. Note, we use a 358-day calendar, i.e. all months have 30 days except for February (28 days).</p>
<p>EUR-LEX. (2008). Directive 2008/50/EC of the European parliament and of the council of 21 May 2008 on ambient air quality and cleaner air for Europe (2008/50/EC). Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32008L0050</p>
<p>Schnell, J. L., Holmes, C. D., Jangam, A., Prather, M. J. (2014). Skill in forecasting extreme ozone pollution episodes with a global atmospheric chemistry model. Atmos Chem Phys, 14(15), 7721-7739. doi:10.5194/acp-14-7721-2014</p>
<p>Schnell, J. L., Prather, M. J., Josse, B., Naik, V., Horowitz, L. W., Cameron-Smith, P., Bergmann, D., Zeng, G., Plummer, D. A., Sudo, K., Nagashima, T., Shindell, D. T., Faluvegi, G., Strode, S. A. (2015). Use of North American and European air quality networks to evaluate global chemistry–climate modeling of surface ozone. Atmos. Chem. Phys., 15(18), 10581-10596. doi:10.5194/acp-15-10581-2015</p>
<p>Schnell, J. L., Prather, M. J. (2017). Co-occurrence of extremes in surface ozone, particulate matter, and temperature over eastern North America. Proceedings of the National Academy of Sciences, 114(11), 2854-2859. doi:10.1073/pnas.1614453114</p>
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